Podcasts about convolutional

Play Episode Listen Later May 1, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.05.01.538952v1?rss=1 Authors: Chattopadhyay, T., Singh, A., Laltoo, E., Boyle, C. P., Owens-Walton, C., Chen, Y.-L., Cook, P., McMillan, C., Tsai, C.-C., Wang, J.-J., Wu, Y.-R., van der Werf, Y., Thompson, P. M. Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Competitive performance and superior noise robustness of a non-negative deep convolutional spiking network

Play Episode Listen Later Apr 24, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.22.537923v1?rss=1 Authors: Rotermund, D., Garcia-Ortiz, A., Pawelzik, K. R. Abstract: Networks of spiking neurons promise to combine energy efficiency with high performance. However, spiking models that match the performance of current state-of-the-art networks while requiring moderate computational resources are still lacking. Here we present an alternative framework to deep convolutional networks (CNNs), the "Spike by Spike" network (SbS), together with an efficient backpropagation algorithm. SbS implements networks based on non-negative matrix factorisation (NNMF), but uses discrete events as signals instead of real values. On clean data, the performance of CNNs is matched by both NNMF-based networks and SbS. SbS are found to be most robust when the data is corrupted by noise, specially when this noise was not seen before. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Human visual cortex and deep convolutional neural network care deeply about object background

Play Episode Listen Later Apr 14, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.14.536853v1?rss=1 Authors: Loke, J., Seijdel, N., Snoek, L., Sörensen, L. K. A., van de Klundert, R., van der Meer, M., Quispel, E., Cappaert, N., Scholte, H. S. Abstract: Deep convolutional neural networks (DCNNs) are able to predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with electroencephalography (EEG) recordings obtained from 62 human subjects during an object categorization task. Previous physiological studies on object categorization have highlighted the importance of figure-ground segregation - the ability to distinguish objects from their backgrounds. Therefore, we set out to investigate if figureground segregation could explain DCNNs predictive power. Using a stimuli set consisting of identical target objects embedded in different backgrounds, we examined the influence of object background versus object category on both EEG and DCNN activity. Crucially, the recombination of naturalistic objects and experimentally-controlled backgrounds creates a sufficiently challenging and naturalistic task, while allowing us to retain experimental control. Our results showed that early EEG activity ( less than 100ms) and early DCNN layers represent object background rather than object category. We also found that the predictive power of DCNNs on EEG activity is related to processing of object backgrounds, rather than categories. We provided evidence from both trained and untrained (i.e. random weights) DCNNs, showing figure-ground segregation to be a crucial step prior to the learning of object features. These findings suggest that both human visual cortex and DCNNs rely on the segregation of object backgrounds and target objects in order to perform object categorization. Altogether, our study provides new insights into the mechanisms underlying object categorization as we demonstrated that both human visual cortex and DCNNs care deeply about object background. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification

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Play Episode Listen Later Mar 31, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.30.534278v1?rss=1 Authors: Nguyen, P., Sooriyaarachchi, J., Huang, Q., Baker, C. L. Abstract: Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linear-nonlinear model. However, it is challenging to analyze phase-invariant complex cells, which require more elaborate models having a combination of nonlinear subunits. Estimating parameters of these models is made more difficult by cortical neurons' trial-to-trial response variability. We develop a simple convolutional neural network method to estimate receptive field models for both simple and complex visual cortex cells from their responses to natural images. The model consists of a spatiotemporal filter, a parameterized rectifier unit (PReLU), and a two-dimensional Gaussian "map" of the receptive field envelope. A single model parameter determines the simple vs. complex nature of the receptive field, capturing complex cell responses as a summation of homogeneous subunits, and collapsing to a linear-nonlinear model for simple type cells. The convolutional method predicts simple and complex cell responses to natural image stimuli as well as grating tuning curves. The model estimates yield a continuum of values for the PReLU parameter across the sampled neurons, showing that the simple/complex nature of cells can vary in a continuous manner. We demonstrate that complex cells respond less reliably than simple cells - compensation for this unreliability reveals good predictive performance on novel sets of natural images, with predictive performance for complex cells proportionately closer to that for simple cells. Most spatial receptive field structures are well fit by Gabor functions, whose parameters confirm well-known properties of cat A17/18 receptive fields. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Robust neural tracking of linguistic speech representations using a convolutional neural network.

Play Episode Listen Later Mar 31, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.30.534911v1?rss=1 Authors: Puffay, C., Vanthornhout, J., Gillis, M., Accou, B., Van hamme, H., Francart, T. Abstract: Objective: When listening to continuous speech, populations of neurons in the brain track different features of the signal. Neural tracking can be measured by relating the electroencephalography (EEG) and the speech signal. Recent studies have shown a significant contribution of linguistic features over acoustic neural tracking using linear models. Linear models cannot model the nonlinear dynamics of the brain. We introduce a convolutional neural network (CNN) that relates EEG to linguistic features using phoneme or word onsets as a control, and has the capacity to model non-linear relations. Approach: We integrate phoneme- and word-based linguistic features (phoneme surprisal, cohort entropy, word surprisal and word frequency) in our nonlinear CNN model and investigate if they carry additional information on top of lexical features (phoneme and word onsets). We compare the results to a linear decoder and a linear CNN, and evaluate the impact of the model's architecture, the presence of linguistic features and the training paradigm on a match-mismatch task performance. Main results: For the non-linear CNN, we found a significant contribution of cohort entropy over phoneme onsets, and of word surprisal and word frequency over word onsets. The training paradigm and architecture have a significant impact on the performance, and the non-linear CNN outperforms the linear baselines on the match-mismatch task. Significance: Measuring coding of linguistic features in the brain is important for auditory neuroscience research and applications that involve objectively measuring speech understanding. With linear models this is measurable, but the effects are very small. The proposed non-linear CNN model yields larger effect sizes and therefore could show effects that would be otherwise unmeasurable, and may in the future lead to improved within-subject measures and shorter recording durations. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis

Play Episode Listen Later Jan 5, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.04.522805v1?rss=1 Authors: Ellis, C. A., Miller, R., Calhoun, V. Abstract: Machine learning methods have frequently been applied to electroencephalography (EEG) data. However, while supervised EEG classification is well-developed, relatively few studies have clustered EEG, which is problematic given the potential for clustering EEG to identify novel subtypes or patterns of dynamics that could improve our understanding of neuropsychiatric disorders. There are established methods for clustering EEG using manually extracted features that reduce the richness of the feature space for clustering, but only a couple studies have sought to use deep learning-based approaches with automated feature learning to cluster EEG. Those studies involve separately training an autoencoder and then performing clustering on the extracted features, and the separation of those steps can lead to poor quality clustering. In this study, we propose an explainable convolutional autoencoder-based approach that combines model training with clustering to yield high quality clusters. We apply the approach within the context of schizophrenia (SZ), identifying 8 EEG states characterized by varying levels of {delta} activity. We also find that individuals who spend more time outside of the dominant state tend to have increased negative symptom severity. Our approach represents a significant step forward for clustering resting-state EEG data and has the potential to lead to novel findings across a variety of neurological and neuropsychological disorders in future years. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Facial representation comparisons between human brain and deep convolutional neural network reveal a fatigue repetition suppression mechanism

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Play Episode Listen Later Jan 3, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.02.522298v1?rss=1 Authors: Lu, Z., Ku, Y. Abstract: Repetition suppression for faces, a phenomenon that neural responses are reduced to repeated faces in the visual cortex, have long been studied. However, the underlying primary neural mechanism of repetition suppression remains debated. In recent years, artificial neural networks can achieve the performance of face recognition at human level. In our current study, we combined human electroencephalogram (EEG) and the deep convolutional neural network (DCNN) and applied reverse engineering to provide a novel way to investigate the neural mechanisms of facial repetition suppression. First, we used brain decoding approach to explore the representations of faces and demonstrates its repetition suppression effect in human brains. Then we constructed two repetition suppression models, Fatigue and Sharpening models, to modify the activation of DCNNs and conducted cross-modal representational similarity analysis (RSA) comparisons between human EEG signals and activations in two modified DCNNs, respectively. We found that representations of human brains were more similar to representations of Fatigue-modified DCNN instead of Sharpening modified DCNN. Our results suggests that the facial repetition suppression effect in face perception is more likely caused by the fatigue mechanism suggesting that the activation of neurons with stronger responses to face stimulus would be attenuated more. Therefore, the current study supports the fatigue mechanism as a more plausible neural mechanism of facial repetition suppression. The comparison between representations in the human brain and DCNN provides a promising tool to simulate and infer the brain mechanism underlying human behaviors. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Neural correlates of face perception modeled with a convolutional recurrent neural network

Play Episode Listen Later Jan 3, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.02.522523v1?rss=1 Authors: O'Reilly, J. A., Wehrman, J., Carey, A., Bedwin, J., Hourn, T., Asadi, F., Sowman, P. F. Abstract: Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We developed a computational model of visual ERP generation to study this phenomenon which consisted of a convolutional neural network (CNN) connected to a recurrent neural network (RNN). We used open-access data to develop the model, generated synthetic images for simulating experiments, then collected additional data to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. The CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events. Cross-validation model outputs strongly correlated with open-access (r = 0.98) and validation study data (r = 0.78). Open-access and validation study data correlated similarly (r = 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Get a new perspective on EEG: Convolutional neural network encoders for parametric t-SNE.

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Play Episode Listen Later Dec 9, 2022

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.08.519691v1?rss=1 Authors: Svantesson, M., Olausson, H., Eklund, A., Thordstein, M. Abstract: Background: t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data is usually first transformed into a set of features, but it is not known which features are optimal. New method: The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. A simple neighbor distribution based on ranked distances was used for the high- dimensional representation instead of the traditional normal distribution. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: 1) wakefulness and sleep, 2) interictal epileptiform discharges, and 3) seizure activity. Results: The CNN encoders for the three datasets produced low-dimensional representations of the da- tasets with a global and local structure that conformed well to the EEG characters and general- ized to new data. Comparison to existing methods: Compared to parametric t-SNE for either a short-time Fourier transforms or wavelet represen- tation of the datasets, the developed CNN encoders performed equally well but generally pro- duced a higher degree of clustering. Conclusions: The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data, e.g., visual summaries of recordings, and trends for continuous EEG monitoring. It might also be used to generate features for other types of machine learning. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data

Astro arXiv | all categories

Play Episode Listen Later Nov 29, 2022 0:54

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data by Irham Taufik Andika et al. on Tuesday 29 November Over the last two decades, around three hundred quasars have been discovered at $zgtrsim6$, yet only one was identified as being strong-gravitationally lensed. We explore a new approach, enlarging the permitted spectral parameter space while introducing a new spatial geometry veto criterion, implemented via image-based deep learning. We made the first application of this approach in a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) pre-selection of the candidates based on their spectral energy distributions (SEDs) using catalog-level photometry and (ii) relative probabilities calculation of being a lens or some contaminant utilizing a convolutional neural network (CNN) classification. The training datasets are constructed by painting deflected point-source lights over actual galaxy images to generate realistic galaxy-quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of $theta_mathrm{E} leq 1$ arcsec. Visual inspection is then performed for sources with CNN scores of $P_mathrm{lens} > 0.1$, which led us to obtain 36 newly-selected lens candidates, waiting for spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs that can overcome the veto limitations of primarily dropout-based SED selection approaches. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.14543v1

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When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data

Astro arXiv | all categories

Play Episode Listen Later Nov 28, 2022 0:54

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data by Irham Taufik Andika et al. on Monday 28 November Over the last two decades, around three hundred quasars have been discovered at $zgtrsim6$, yet only one was identified as being strong-gravitationally lensed. We explore a new approach, enlarging the permitted spectral parameter space while introducing a new spatial geometry veto criterion, implemented via image-based deep learning. We made the first application of this approach in a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) pre-selection of the candidates based on their spectral energy distributions (SEDs) using catalog-level photometry and (ii) relative probabilities calculation of being a lens or some contaminant utilizing a convolutional neural network (CNN) classification. The training datasets are constructed by painting deflected point-source lights over actual galaxy images to generate realistic galaxy-quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of $theta_mathrm{E} leq 1$ arcsec. Visual inspection is then performed for sources with CNN scores of $P_mathrm{lens} > 0.1$, which led us to obtain 36 newly-selected lens candidates, waiting for spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs that can overcome the veto limitations of primarily dropout-based SED selection approaches. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.14543v1

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Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed using Convolutional Neural Network

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Play Episode Listen Later Nov 22, 2022

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.21.517434v1?rss=1 Authors: Moinuddin, K. A., Havugimana, F., Al-Fahad, R., Bidelman, G., Yeasin, M. Abstract: The process of categorizing sounds into distinct phonetic categories is known as categorical perception (CP). Response times (RTs) provide a measure of perceptual difficulty during labeling decisions (i.e., categorization). The RT is quasi-stochastic in nature due to individuality and variations in perceptual tasks. To identify the source of RT variation in CP, we have built models to decode the brain regions and frequency bands driving fast, medium, and slow response decision speeds. In particular, we implemented parameter optimized convolutional neural network (CNN) to classify listeners' behavioral RTs from their neural EEG data. We adopted visual interpretation of model response using Guided-GradCAM to identify spatial-spectral correlates of RT. Our framework includes (but is not limited to): (i) a data augmentation technique designed to reduce noise and control the overall variance of EEG dataset; (ii) bandpower topomaps to learn the spatial-spectral representation using CNN; (iii) large-scale Bayesian hyper-parameter optimization to find best performing CNN model; (iv) ANOVA and post-hoc analysis on Guided-GradCAM activation values to measure the effect of neural regions and frequency bands on behavioral responses. Using this framework, we observe that - {beta} (10-20 Hz) activity over left frontal, right prefrontal/frontal, and right cerebellar regions are correlated with RT variation. Our results indicate that attention, template matching, temporal prediction of acoustics, motor control, and decision uncertainty are the most probable factors in RT variation. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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What Makes Convolutional Models Great on Long Sequence Modeling?

Papers Read on AI

Play Episode Listen Later Nov 2, 2022 28:07

Convolutional models have been widely used in multiple domains. However, most existing models only use local convolution , making the model unable to handle long-range dependency eﬃciently. Attention overcomes this problem by aggregating global information based on the pair-wise attention score but also makes the computational complexity quadratic to the sequence length. S4 can be eﬃciently implemented as a global convolutional model whose kernel size equals the input sequence length. With Fast Fourier Transform, S4 can model much longer sequences than Transformers and achieve signiﬁcant gains over SoTA on several long-range tasks. Despite its empirical success, S4 is involved. It requires sophis-ticated parameterization and initialization schemes that combine the wisdom from several prior works. As a result, S4 is less intuitive and hard to use for researchers with limited prior knowledge. Here we aim to demystify S4 and extract basic principles that contribute to the success of S4 as a global convolutional model. 2022: Yuhong Li, Tianle Cai, Yi Zhang, De-huai Chen, Debadeepta Dey https://arxiv.org/pdf/2210.09298v1.pdf

Building A Business Powered By Machine Learning At Assembly AI

The Machine Learning Podcast

Play Episode Listen Later Sep 9, 2022 58:42

Summary The increasing sophistication of machine learning has enabled dramatic transformations of businesses and introduced new product categories. At Assembly AI they are offering advanced speech recognition and natural language models as an API service. In this episode founder Dylan Fox discusses the unique challenges of building a business with machine learning as the core product. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Dylan Fox about building and growing a business with ML as its core offering Interview Introduction How did you get involved in machine learning? Can you describe what Assembly is and the story behind it? For anyone who isn’t familiar with your platform, can you describe the role that ML/AI plays in your product? What was your process for going from idea to prototype for an AI powered business? Can you offer parallels between your own experience and that of your peers who are building businesses oriented more toward pure software applications? How are you structuring your teams? On the path to your current scale and capabilities how have you managed scoping of your model capabilities and operational scale to avoid getting bogged down or burnt out? How do you think about scoping of model functionality to balance composability and system complexity? What is your process for identifying and understanding which problems are suited to ML and when to rely on pure software? You are constantly iterating on model performance and introducing new capabilities. How do you manage prototyping and experimentation cycles? What are the metrics that you track to identify whether and when to move from an experimental to an operational state with a model? What is your process for understanding what’s possible and what can feasibly operate at scale? Can you describe your overall operational patterns delivery process for ML? What are some of the most useful investments in tooling that you have made to manage development experience for your teams? Once you have a model in operation, how do you manage performance tuning? (from both a model and an operational scalability perspective) What are the most interesting, innovative, or unexpected aspects of ML development and maintenance that you have encountered while building and growing the Assembly platform? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Assembly? When is ML the wrong choice? What do you have planned for the future of Assembly? Contact Info @YouveGotFox on Twitter LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Assembly AI Podcast.__init__ Episode Learn Python the Hard Way NLTK NLP == Natural Language Processing NLU == Natural Language Understanding Speech Recognition Tensorflow r/machinelearning SciPy PyTorch Jax HuggingFace RNN == Recurrent Neural Network CNN == Convolutional Neural Network LSTM == Long Short Term Memory Hidden Markov Models Baidu DeepSpeech CTC (Connectionist Temporal Classification) Loss Model Twilio Grid Search K80 GPU A100 GPU TPU == Tensor Processing Unit Foundation Models BLOOM Language Model DALL-E 2 The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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Lenia and Expanded Universe

Papers Read on AI

Play Episode Listen Later Oct 12, 2021 30:45

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence. 2020: B. Chan Genetic algorithm, Artificial intelligence, Convolutional neural network, Artificial life, Self-organization, Cellular automaton, Self-replication, Artificial neural network, Polyhedron, Organizing (structure), Autonomous robot, Emergence, Recurrent neural network, Semiconductor industry https://arxiv.org/pdf/2005.03742v1.pdf

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HPR3319: Linux Inlaws S01E28: Politicians and artificial intelligence part 1

Hacker Public Radio

Play Episode Listen Later Apr 22, 2021

In this episode, our two heroes explore the realm of artificial intelligence, paying special attention to deep learning (hoping that some of the stuff may rub on them :-). In this first part of a three-part mini-series the chaps discuss the foundation including networks, neurons and other topics of advanced black magic, carefully avoiding the temptations of introducing too much maths (we'll leave this to the Grumpy Old Coders :-). Links: Artificial intelligence: https://en.wikipedia.org/wiki/Artificial_intelligence Machine learning: https://www.mygreatlearning.com/blog/machine-learning-tutorial Deep learning: https://www.guru99.com/deep-learning-tutorial.html Artificial neural networks (ANN): https://www.asimovinstitute.org/neural-network-zoo Back-propagation ANNs (BPN): https://en.wikipedia.org/wiki/Backpropagation DWAVE: https://www.dwavesys.com/quantum-computing Convolutional neural networks (CNNs): https://en.wikipedia.org/wiki/Convolutional_neural_network Generative adversarial network (GAN): https://en.wikipedia.org/wiki/Generative_adversarial_network Spy vs. Spy: http://toonopedia.com/spyvsspy.htm Atlantik Ale: https://www.stoertebeker.com/stoertebeker-atlantik-ale.html

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Tesla's FSD Hardware Advantage w/ James Douma #9 (Ep. 274)

Dave Lee on Investing

Play Episode Listen Later Mar 18, 2021 51:44

I'm joined by James Douma as we discuss Tesla's approach to FSD hardware and why it's built for success. View part 2 of this conversation, https://youtu.be/CFtBdWN3t_Y . James Douma on Twitter: https://twitter.com/jamesdouma James Douma Playlist, https://www.youtube.com/watch?v=iMtujONU_0I&list=PLfibpgBinf9R7KIedEU3y-YjrA63LSKHX Timestamps 00:00 - Introduction 0:39 - Does Tesla FSD have a hardware advantage?* 6:58 - Tesla's made the best chip for FSD 8:20 - Energy consumption of FSD chip 12:15 - 2300 frames per second 17:40 - What will the next generation chip be used for? 21:23 - Will the next generation chip be a drop in replacement? 22:17 - How will the next generation chip be rolled out? 28:55 - Convolutional neurons 36:15 - Back propagation and self fixing Neural Nets 41:20 - Human inputs 45:12 - What resolution are the images? 48:22 - What percent of processing capability is needed to run FSD? 50:15 - Conclusion Social

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Tesla’s FSD Hardware Advantage Part 1 w/ James Douma (Ep. 274)

Dave Lee on Investing

Play Episode Listen Later Mar 18, 2021 51:44

I'm joined by James Douma as we discuss Tesla's approach to FSD hardware and why it's built for success. James Douma on Twitter: https://twitter.com/jamesdouma James Douma Playlist, https://www.youtube.com/watch?v=iMtujONU_0I&list=PLfibpgBinf9R7KIedEU3y-YjrA63LSKHX Timestamps 00:00 - Introduction 0:39 - Does Tesla FSD have a hardware advantage?* 6:58 - Tesla's made the best chip for FSD 8:20 - Energy consumption of FSD chip 12:15 - 2300 frames per second 17:40 - What will the next generation chip be used for? 21:23 - Will the next generation chip be a drop in replacement? 22:17 - How will the next generation chip be rolled out? 28:55 - Convolutional neurons 36:15 - Back propagation and self fixing Neural Nets 41:20 - Human inputs 45:12 - What resolution are the images? 48:22 - What percent of processing capability is needed to run FSD? 50:15 - Conclusion Social

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Tesla’s FSD Hardware Advantage Part 1 w/ James Douma (Ep. 274)

Dave Lee on Investing

Play Episode Listen Later Mar 18, 2021 51:44

spotify energy investing tesla human apple podcast advantage hardware sq tsla supercharger dave lee fsd douma soundwise convolutional

Artificial Intelligence in Veterinary Practice

Focal Point: the IMV imaging podcast

Play Episode Listen Later Feb 27, 2021 37:13

Links https://www.vetport.com/artificial-intelligence-in-veterinary-medicine https://www.veterinarypracticenews.com/ai-diagnostics-january-2020/ https://www.vetpartners.org/how-can-ai-improve-veterinary-medicine/ https://www.veteldiagnostics.com/artificial-intelligence-now-required-practice-good-medicine https://www.aavr.org/index.php?option=com_content&view=article&id=99&Itemid=263 https://ivcjournal.com/veterinary-radiology-ai/ https://vetology.ai/ https://www.vetmed.ucdavis.edu/news/veterinarians-use-artificial-intelligence-aid-diagnosis-addisons-disease https://cedar.ucdavis.edu/ https://www.signalpet.com/products/signalray/ Convolutional neural network - Wikipedia How We Do It – Atomwise https://stanfordmlgroup.github.io/competitions/chexpert/ https://www.kaggle.com/nih-chest-xrays/data https://mimic-cxr.mit.edu/ https://physionet.org/content/mimic-cxr/2.0.0/ https://www.nature.com/articles/s41597-019-0322-0 https://github.com/MIT-LCP/mimic-cxr https://qure.ai/ https://pytorch.org/hub/pytorch_vision_googlenet/ https://pytorch.org/hub/pytorch_vision_resnet/ https://www.nlm.nih.gov/healthit/snomedct/index.html https://en.wikipedia.org/wiki/Artificial_intelligence https://medium.com/fintechexplained/neural-networks-activation-function-to-back-propagation-understanding-neural-networks-bdd036c3f29f https://medium.com/fintechexplained/neural-networks-bias-and-weights-10b53e6285da Contact Usemail: clinical@imv-imaging.comwebsite: www.imv-imaging.co.uk

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CAMP: a Convolutional Attention-based Neural Network for Multifaceted Peptide-protein Interaction Prediction

Play Episode Listen Later Nov 16, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.16.384784v1?rss=1 Authors: Lei, Y., Li, S., Liu, Z., Wan, F., Tian, T., Li, S., Zhao, D., Zeng, J. Abstract: Peptide-protein interactions (PepPIs) are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. To facilitate the peptide drug discovery process, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Although several deep-learning-based frameworks have been proposed to predict compound-protein interactions or protein-protein interactions, few of them are particularly designed to specifically predict peptide-protein interactions. In this paper, We present a sequence-based Convolutional Attention-based neural network for Multifaceted prediction of Peptide-protein interactions, called CAMP, including predicting binary peptide-protein interactions and corresponding binding residues in the peptides. We also construct a benchmark dataset containing high-quality peptide-protein interaction pairs with the corresponding peptide binding residues for model training and evaluation. CAMP incorporates convolution neural network architectures and attention mechanism to fully exploit informative sequence-based features, including secondary structures, physicochemical properties, intrinsic disorder features and position-specific scoring matrix of the protein. Systematical evaluation of our benchmark dataset demonstrates that CAMP outperforms the state-of-the-art baseline methods on binary peptide-protein interaction prediction. In addition, CAMP can successfully identify the binding residues involved non-covalent interactions for peptides. These results indicate that CAMP can serve as a useful tool in peptide-protein interaction prediction and peptide binding site identification, which can thus greatly facilitate the peptide drug discovery process. The source code of CAMP can be found in https://github.com/twopin/CAMP. Copy rights belong to original authors. Visit the link for more info

predictions attention camp copy li protein interaction liu peptides zhao wan multifaceted neural networks tian zeng biorxiv convolutional

Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

Play Episode Listen Later Nov 11, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.10.376970v1?rss=1 Authors: Gong, W., Beckmann, C. F., Vedaldi, A., Smith, S. M., Peng, H. Abstract: Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject's predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject's age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal. Copy rights belong to original authors. Visit the link for more info

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Predicting drug resistance in M. tuberculosis using a Long-term Recurrent Convolutional Networks architecture

long term architecture copy predicting networks existing tb tuberculosis mtb snp recurrent mycobacterium wgs drug resistance biorxiv forna convolutional

Play Episode Listen Later Nov 8, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.07.372136v1?rss=1 Authors: Safari, A. H., Sedaghat, N., Forna, A., Zabeti, H., Chindelevitch, L., Libbrecht, M. Abstract: Drug resistance in Mycobacterium tuberculosis (MTB) may soon be a leading worldwide cause of death. One way to mitigate the risk of drug resistance is through methods that predict drug resistance in MTB using whole-genome sequencing (WGS) data. Existing machine learning methods for this task featurize the WGS data from a given bacterial isolate by defining one input feature per SNP. Here, we introduce a gene-centric method for predicting drug resistance in TB. We define one feature per gene according to the number of mutations in that gene in a given isolate. This representation greatly decreases the number of model parameters. We further propose a model that considers both gene order through a Long-term Recurrent Convolutional Network (LRCN) architecture, which combines convolutional and recurrent layers. We find that using these strategies yields a substantial, statistically-significant improvement over the state-of-the-art and that this improvement is driven by the order of genes in the genome and their organization into operons. Copy rights belong to original authors. Visit the link for more info

A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data

data driven copy mouse biorxiv network architecture buice convolutional

Play Episode Listen Later Oct 25, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.23.353151v1?rss=1 Authors: Shi, J., Buice, M. A., Shea-Brown, E., Mihalas, S., Tripp, B. P. Abstract: Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connectome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research. Copy rights belong to original authors. Visit the link for more info

High precision in microRNA prediction: a novel genome-wide approach based on convolutional deep residual networks

deep predictions wide networks precision genome residual rnas microrna biorxiv mirnas convolutional

Play Episode Listen Later Oct 25, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.23.352179v1?rss=1 Authors: Yones, C. A., Raad Macchiaroli, J., Bugnon, L., Milone, D. H., Stegmayer, G. Abstract: Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that have a key role in the regulation of gene expression. The importance of miRNAs is widely acknowledged by the community nowadays, and the precise prediction of novel candidates with computational methods is still very needed. This could be done by searching homologous with sequence alignment tools, but this will be restricted only to sequences very similar to the known miRNA precursors (pre-miRNAs). Furthermore, other important properties of pre-miRNAs, such as the secondary structure, are not taken into account by these methods. Many machine learning approaches were proposed in the last years to fill this gap, but these methods were tested in very controlled conditions, which are not fulfilled, for example, when predicting in newly sequenced genomes, where no miRNAs are known. If these methods are used under real conditions, the precision achieved is far from the one published. Results: This work provides a novel approach for dealing with the computational prediction of pre-miRNAs: a convolutional deep residual neural network. The proposed model has been tested on several complete genomes of animals and plants, achieving a precision up to 5 times higher than other approaches at the same recall rates. Also, a novel validation methodology is used to ensure that the performance reported can be achieved when using the method on new unknown species. Availability: To provide fast an easy access to mirDNN, a web demo is available in http://sinc.unl.edu.ar/web-demo/mirdnn/. It can process fasta files with multiple sequences to calculate the prediction scores, and can generate the nucleotide importance plots. The full source code of this project is available at http://sourceforge.net/projects/sourcesinc/files/mirdnn Contact: cyones@sinc.unl.edu.ar Copy rights belong to original authors. Visit the link for more info

Multidimensional face representation in deep convolutional neural network reveals the mechanism underlying AI racism

Play Episode Listen Later Oct 21, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.20.347898v1?rss=1 Authors: Tian, J., Xie, H., Hu, S., Liu, J. Abstract: The increasingly popular application of AI runs the risks of amplifying social bias, such as classifying non-white faces to animals. Recent research has attributed the bias largely to data for training. However, the underlying mechanism is little known, and therefore strategies to rectify the bias are unresolved. Here we examined a typical deep convolutional neural network (DCNN), VGG-Face, which was trained with a face dataset consisting of more white faces than black and Asian faces. The transfer learning result showed significantly better performance in identifying white faces, just like the well-known social bias in human, the other-race effect (ORE). To test whether the effect resulted from the imbalance of face images, we retrained the VGG-Face with a dataset containing more Asian faces, and found a reverse ORE that the newly-trained VGG-Face preferred Asian faces over white faces in identification accuracy. In addition, when the number of Asian faces and white faces were matched in the dataset, the DCNN did not show any bias. To further examine how imbalanced image input led to the ORE, we performed the representational similarity analysis on VGG-Face's activation. We found that when the dataset contained more white faces, the representation of white faces was more distinct, indexed by smaller ingroup similarity and larger representational Euclidean distance. That is, white faces were scattered more sparsely in the representational face space of the VGG-Face than the other faces. Importantly, the distinctiveness of faces was positively correlated with the identification accuracy, which explained the ORE observed in the VGG-Face. In sum, our study revealed the mechanism underlying the ORE in DCNNs, which provides a novel approach of study AI ethics. In addition, the face multidimensional representation theory discovered in human was found also applicable to DCNNs, advocating future studies to apply more cognitive theories to understand DCNN's behavior. Copy rights belong to original authors. Visit the link for more info

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Epileptic Spike Detection by Using a Linear-Phase Convolutional Neural Network

cnn phase copy spike experimental detection linear eeg tanaka neural networks yoshida nakajima epileptic biorxiv convolutional

Play Episode Listen Later Oct 9, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.08.330936v1?rss=1 Authors: Fukumori, K., Yoshida, N., Sugano, H., Nakajima, M., Tanaka, T. Abstract: To cope with the lack of highly skilled professionals, machine leaning with proper signal techniques is a key to establishing automated diagnostic-aid technologies to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with appropriate passbands is essential to enhance biomarkers[-]such as epileptic spike waves[-]that are noted in the EEG. This paper introduces a novel class of convolutional neural networks (CNNs) having a bank of linear-phase finite impulse response filters at the first layer. These may behave as bandpass filters that extract biomarkers without destroying waveforms because of linear-phase condition. The proposed CNNs were trained with a large amount of clinical EEG data, including 15,899 epileptic spike waveforms recorded from 50 patients. These have been labeled by specialists. Experimental results show that the trained data-driven filter bank with supervised learning is dyadic like discrete wavelet transform. Moreover, the area under the curve achieved above 0.9 in most cases. Copy rights belong to original authors. Visit the link for more info

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

Play Episode Listen Later Oct 7, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.05.326140v1?rss=1 Authors: Li, Y., Zhang, C., Bell, E. W., Zheng, W., Zhou, X., Yu, D.-J., Zhang, Y. Abstract: The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP and CAMEO experiments, and outperformed other state-of-the-art methods by at least 58.4% for the CASP 11&12 and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library. Availability: The training and testing data, standalone package, and the online server for TripletRes are available at https://zhanglab.ccmb.med.umich.edu/TripletRes/. Copy rights belong to original authors. Visit the link for more info

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A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Play Episode Listen Later Oct 1, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.29.318642v1?rss=1 Authors: Ke, Z., Vikalo, H. Abstract: Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods. Copy rights belong to original authors. Visit the link for more info

auto experiments viral copy assembly existing reconstruction np biorxiv encoder convolutional haplotype

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

Play Episode Listen Later Sep 28, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.25.313668v1?rss=1 Authors: Ahmed, S., Muhammod, R., Adilina, S., Khan, Z. H., Shatabda, S., Dehzangi, A. Abstract: Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification through simultaneous interaction with different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN. Copy rights belong to original authors. Visit the link for more info

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DeepInsight-FS: Selecting features for non-image data using convolutional neural network

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Play Episode Listen Later Sep 19, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.17.301515v1?rss=1 Authors: Sharma, A., Lysenko, A., Boroevich, K., Vans, E., Tsunoda, T. Abstract: Identifying smaller element or gene subsets from biological or other data types is an essential step in discovering underlying mechanisms. Statistical machine learning methods have played a key role in revealing gene subsets. However, growing data complexity is pushing the limits of these techniques. A review of the recent literature shows that arranging elements by similarity in image-form for a convolutional neural network (CNN) improves classification performance over treating them individually. Expanding on this, here we show a pipeline, DeepInsight-FS, to uncover gene subsets of clinical relevance. DeepInsight-FS converts non-image samples into image-form and performs element selection via CNN. To our knowledge, this is the first approach to employ CNN for element or gene selection on non-image data. A real world application of DeepInsight-FS to publicly available cancer data identified gene sets with significant overlap to several cancer-associated pathways suggesting the potential of this method to discover biomedically meaningful connections. Copy rights belong to original authors. Visit the link for more info

scGCN: a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics

leveraging algorithms copy networks zhang graphs knowledge transfer single cell omics biorxiv convolutional

Play Episode Listen Later Sep 14, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.13.295535v1?rss=1 Authors: Song, Q., Su, J., Zhang, W. Abstract: Single-cell omics represent the fastest-growing genomics data type in the literature and the public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of the single-cell omics. The current label transfer methods have limited performance, largely due to the intrinsic heterogeneity and extrinsic differences between datasets. Here, we present a robust graph-based artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Benchmarked with other label transfer methods on totally 30 single cell omics datasets, scGCN has consistently demonstrated superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN. Copy rights belong to original authors. Visit the link for more info

Modeling the hemodynamic response function using simultaneous EEG-fMRI data and convolutional sparse coding analysis with rank-1 constraints

Play Episode Listen Later Sep 10, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.09.290296v1?rss=1 Authors: Prokopiou, P., Kassinopoulos, M., Xifra-Porxas, A., Boudrias, M.-H., Mitsis, G. D. Abstract: Over the last few years, an increasing body of evidence points to the hemodynamic response function as an important confound of resting-state functional connectivity. Several studies in the literature proposed using blind deconvolution of resting-state fMRI data to retrieve the HRF, which can be subsequently used for hemodynamic deblurring. A basic hypothesis in these studies is that relevant information of the resting-state brain dynamics is condensed in discrete events resulting in large amplitude peaks in the BOLD signal. In this work, we showed that important information of resting-state activity, in addition to the larger amplitude peaks, is also concentrated in lower amplitude peaks. Moreover, due to the strong effect of physiological noise and head motion on the BOLD signal, which in many cases may not be completely removed after preprocessing, the neurophysiological origin of the large amplitude BOLD signal peaks is questionable. Hence, focusing on the large amplitude BOLD signal peaks may yield biased HRF estimates. To define discrete events of neuronal origins, we proposed using simultaneous EEG-fMRI along with convolutional sparse coding analysis. Our results suggested that events detected in the EEG are able to describe the slow oscillations of the BOLD signal and to obtain consistent HRF shapes across subjects under both task-based and resting-state conditions. Copy rights belong to original authors. Visit the link for more info

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Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Play Episode Listen Later Sep 3, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.02.278804v1?rss=1 Authors: zeng, y., zhou, x., rao, j., lu, y., yang, y. Abstract: Recent advances in single-cell RNA sequencing (scRNA-seq) technologies provide a great opportunity to study gene expression at cellular resolution, and the scRNA-seq data has been routinely conducted to unfold cell heterogeneity and diversity. A critical step for the scRNA-seq analyses is to cluster the same type of cells, and many methods have been developed for cell clustering. However, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed a new method (GraphSCC) to cluster cells based on scRNA-seq data by accounting structural relations between cells through a graph convolutional network. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. Extensive experiments indicate that GraphSCC model outperforms state-of-the-art methods in various evaluation metrics on both simulated and real datasets. Further visualizations show that GraphSCC provides representations for better intra-cluster compactness and inter-cluster separability. Copy rights belong to original authors. Visit the link for more info

data network copy relations capturing cells rna structural extensive graphs clustering single cell biorxiv rnaseq convolutional abstract recent

Three-dimensional convolutional autoencoder extracts features of structural brain images with a diagnostic label-free approach: Application to schizophrenia datasets

Play Episode Listen Later Aug 25, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.24.213447v1?rss=1 Authors: Yamaguchi, H., Hashimoto, Y., Sugihara, G., Miyata, J., Murai, T., Takahashi, H., Honda, M., Hishimoto, A., Yamashita, Y. Abstract: There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS), and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets including 71 SZ patients and 71 HS. The proposed 3D-CAEs were successfully reconstructed into high-resolution 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relevant clinical information. We explored the appropriate hyper parameter range of 3D-CAE, and it was suggested that a model with eight convolution layers might be relevant to extract features for predicting the dose of medication and symptom severity in schizophrenia. Copy rights belong to original authors. Visit the link for more info

Learning Patterns of the Ageing Brain in MRI using Deep Convolutional Networks

Play Episode Listen Later Aug 17, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.17.253732v1?rss=1 Authors: Dinsdale, N. K., Bluemke, E., Smith, S. M., Arya, Z., Vidaurre, D., Jenkinson, M., Namburete, A. I. L. Abstract: Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors {Delta}Brain Age = AgePredicted - AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between {Delta}Brain Age and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the {Delta}Brain Age from models such as this network were predictive of any health outcomes. Copy rights belong to original authors. Visit the link for more info

learning deep brain cnn patterns copy networks mri ageing t1 jenkinson idps uk biobank biorxiv convolutional

VirPreNet: A weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all 8 segments

Play Episode Listen Later Jul 31, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.31.230904v1?rss=1 Authors: Yin, R., Luo, Z., Zhuang, P., Lin, Z., Kwoh, C. K. Abstract: Motivation: Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics. Results: In this paper, we propose a weighted ensemble convolutional neural network for the virulence prediction of influenza A viruses named VirPreNet that uses all 8 segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the 8-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble convolutional neural network is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet's main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence. Copy rights belong to original authors. Visit the link for more info

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Synaptic dynamics as convolutional units

Play Episode Listen Later Jun 5, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.04.133892v1?rss=1 Authors: Rossbroich, J., Trotter, D., Toth, K., Naud, R. Abstract: Synaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Copy rights belong to original authors. Visit the link for more info

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CoNNECT: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains

Machine Learning in Healthcare, by Skin Analytics

Play Episode Listen Later May 5, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.05.078089v1?rss=1 Authors: Endo, D., Kobayashi, R., Bartolo, R., Averbeck, B. B., Sugase-Miyamoto, Y., Hayashi, K., Kenji, K., Richmond, B. J., Shinomoto, S. Abstract: The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians, because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms, GLMCC. Although the GLMCC algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another algorithm using a convolutional neural network for estimating synaptic connectivity from spike trains, CoNNECT. After adaptation to very large amounts of simulated data, this algorithm robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new algorithm, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys. Copy rights belong to original authors. Visit the link for more info

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#005: Causation & Correlation with Artificial Intelligence

Play Episode Listen Later Mar 16, 2020 15:49

This week, Neil is joined by Dr. Jack Greenhalgh, AI director at Skin Analytics to discuss causation and correlation. Featured this week: The difference between causation and correlation and their relevance when it comes to AI How death by drowning in swimming pools correlates with movies starring Nicholas Cage and why that's relevant when it comes to understanding machine learning The risk of overfitting and the best ways to prevent falling into traps Convolutional neural networks and Jan Le Cun’s tweets! And again we highlight why prospective studies are so important Visit us at: https://skin-analytics.com/ Get in touch with Neil: neil@skinanalytics.co.uk | Linkedin: www.linkedin.com/in/ndaly/

ai artificial intelligence nicholas cage correlation causation convolutional

Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram

JACC Podcast

Play Episode Listen Later Feb 18, 2020 9:50

Commentary by Dr. Valentin Fuster

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12 - Deep Learning 2019/2020

Deep Learning 2019/2020 (QHD 1920 - Video & Folien)

Play Episode Listen Later Jan 20, 2020 70:46

training confidence loss network context region networks object layer detection output classification deep learning segmentation bounding convolutional

Ep. 012, Rise of the machines

Underserved

Play Episode Listen Later Nov 20, 2019 38:50

How many people started their data science career at 16 years old? Then took a sabbatical to play with famous jazz musicians? And subsequently bootstrapped the US presence for a Norwegian data science firm? I know one! His name is Russ Wilcox. On this week's Underserved Russel explains how a Tesla sees the world (and how it can be fooled), how to teach an old AI new tricks, and we discuss the ethical implications of artificial intelligence. If you ever wondered how machine learning worked, or if the rise of SKYNET is imminent, this is the podcast for you. Russel's company, Sannsyn US Sannsyn.com/us Gradient Descent: https://en.wikipedia.org/wiki/Gradient_descent Convolutional neural networks: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks Anders Hanssen: https://www.mn.uio.no/math/personer/vit/anderch/ Tensorflow: https://www.tensorflow.org/ Keras: https://keras.io/ Pytorch: https://pytorch.org/ Comparison of TF, Keras, PT: https://dzone.com/articles/tensorflow-vs-pytorch-vs-keras-for-nlp Anthony Fung: https://www.anthonyfungmusic.com/ George Garzone: http://www.georgegarzone.com/ The Russ Wilcox Jazz Group : https://www.facebook.com/rwilcoxjazz/

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Human-Centered AI with Di Dang

Google Cloud Platform Podcast

Play Episode Listen Later May 7, 2019 38:04

Mark Mirchandani and Michelle Casbon take over the show this week to discuss AI and the PAIR Guidebook to Human-Centered AI. Mark Mandel pops in on the interview, and Di Dang, Design Advocate at Google, talks about her role in designing and building the guidebook with the intent of helping others create quality AI projects. Di describes human-centered AI as a practice of not only being conscious of the project being built, but also considering how this AI project will impact us as humans at the end of the day. We influence machine learning so much, both intentionally and unintentionally, and it’s our job to look at the project and results as a whole. In the guidebook, topics like data bias in machine learning, what design patterns work, how to establish trust with the user, and more are addressed. Di explains that the guidebook is a work in progress that will develop with input from users and advances in technology. Di Dang Di Dang recently joined Google’s Design Relations team as a Design Advocate supporting emerging technologies such as augmented reality and machine learning. Previously, she worked as a Senior UX Designer and led the Emerging Tech group at Seattle-based digital agency POP, advising clients on how VR/AR, web/mobile, conversational UI, and machine learning could benefit their end users. With a degree in Philosophy and Religion, she considers herself an optimistic realist who is passionate about ethical design. You can find Di onstage doing improv or on Twitter @dqpdang. Cool things of the week Bringing the best of open source to Google Cloud customers blog James Ward’s Cloud Run button site Michelle’s favorite codelabs from I/O TPU-speed data pipelines site Your first Keras model site Convolutional neural networks site Modern convnets, squeezenet, with Keras with TPUs site Interview People + AI Guidebook site PAIR site GCP Podcast Episode 114: Machine Learning Bias and Fairness with Timnit Gebru and Margaret Mitchell podcast Machine Learning Crash Course site Google Clips site Google Brain Team site Question of the week How do I get started with practical AI? Build an Appointment Scheduler Chatbot with Dialogflow Where can you find us next? Michelle will be at Google I/O and Kubecon Europe. No I/O event in your area? You can host one!

Deep Learning for Population Genetic Inference with Dan Schrider - TWiML Talk #249

This Week in Machine Learning & Artificial Intelligence (AI) Podcast

Play Episode Listen Later Apr 8, 2019 49:53

Today we’re joined by Dan Schrider, assistant professor in the department of genetics at The University of North Carolina at Chapel Hill. My discussion with Dan starts with an overview of population genomics and from there digs into his application of machine learning in the field, allowing us to, for example, better understand population size changes and gene flow from DNA sequences. We then dig into Dan’s paper “The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference,” which was published in the Molecular Biology and Evolution journal, which examines the idea that CNNs are capable of outperforming expert-derived statistical methods for some key problems in the field. Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off. The complete show notes for this episode can be found at https://twimlai.com/talk/249.

#52 – Convolutional Color Constancy

Misreading Chat

Play Episode Listen Later Mar 7, 2019

機械学習を使ったホワイトバランスのアルゴリズムについて森田が話します。

color convolutional

#9 AI in India, US and Japan - with Gautam Bajaj

Dataspaning

Play Episode Listen Later Dec 5, 2018 57:35

This episode features Gautam Bajaj, an engineer and data scientist that has worked with technologies mostly related to AI and machine learning. He has worked in India, USA, a leading video game company in Japan and is now consulting in Tokyo. We talk about getting into the field of data science and AI and about the different working cultures in Japan, India, USA and Sweden. We get into the possibilities and challenges of AI and machine learning, such as scaling and keeping up with developments in such a rapidly evolving field. Some things mentioned in this episode:- YanLeCun, AI course- Hadoop: a framework for distributed processing and big data- Kubernetes: a tool for running applications in "the cloud"- Medium, a blog platform- Recommender/recommendation system- Convolutional neural network (CNN)- Reinforcement learning- Generative adversarial networks (GANs)- Python- Amazon Web Services (AWS): cloud, web hosting etc.- Google Cloud Platform (GCP): cloud, web hosting etc. We do not currently have any external partners and all opinions expressed are solely our own. Nothing discussed on this podcast should be considered as any kind of investment advice. In this episode:- Gautam Bajaj, gautam1237 at gmail dot com- Martin Nordgren, works at Tobii, former engineer at Dirac, @martinjnordgren Contact us:dataspaning.se@dataspaning @ Twitterdataspaning@gmail.com

united states ai japan tokyo sweden medium generative kubernetes gautam bajaj hadoop dirac india us tobii recommender google cloud platform gcp convolutional

Graph Analytic Systems with Zachary Hanif - TWiML Talk #188

This Week in Machine Learning & Artificial Intelligence (AI) Podcast

Play Episode Listen Later Oct 8, 2018 55:29

In this, the final episode of our Strata Data Conference series, we’re joined by Zachary Hanif, Director of Machine Learning at Capital One’s Center for Machine Learning. Zach led a session at Strata called “Network effects: Working with modern graph analytic systems,” which we had a great chat about back in New York. We start our discussion with a look at the role of graph analytics in the machine learning toolkit, including some important application areas for graph-based systems. We continue with an overview of the different ways to implement graph analytics, with a particular emphasis on the emerging role of what he calls graphical processing engines which excel at handling large datasets. We also discuss the relationship between these kinds of systems and probabilistic graphical models, graphical embedding models, and graph convolutional networks in deep learning. The complete show notes for this episode can be found at twimlai.com/talk/188. For more information on the Strata Data Conference series, visit twimlai.com/stratany2018.

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Startups vs. Traditional Industry

The AI Element

Play Episode Listen Later Jul 24, 2018 25:39

As AI seeps into every industry, businesses are being forced to adapt. Old school industries may not be as lean or quick to pivot as startups, but they have access to a motherlode of funding and data. Still, red tape and outdated infrastructure may block them from the timely AI transformation they need to stay afloat tomorrow. Alex Shee speaks with serial AI entrepreneur JF Gagné about this tension between startups and more corporate environments. Then, 15-year veteran of the insurance industry Natacha Mainville shares some real-world examples of how AI is flipping the industry on its head, forcing incumbents to keep up. Featured in this episode: JF Gagné, CEO of Element AI Natacha Mainville, Chief Innovation Officer at TandemLaunch Mentioned in the episode: JDA Software, retail and supply chain solutions Element AI, AI solutions provider Convolutional neural networks (Wikipedia) Lemonade Renters & Home Insurance, insurance startup

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Convolutional nets are great for image classification... if this were 2016. But it's 2018 and Canada's greatest neural networker Geoff Hinton has some new ideas, namely capsule networks. Capsule nets are a completely new type of neural net architecture designed to do image classification on far fewer training cases than convolutional nets, and they're posting results that are competitive with much more mature technologies. In this episode, we'll give a light conceptual introduction to capsule nets and get geared up for a future episode that will do a deeper technical dive.

canada learning science data networks linear capsule conceptual digressions geoff hinton convolutional

Convolutional Neural Nets

Linear Digressions

Play Episode Listen Later Apr 1, 2018 21:55

If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

learning science data nets linear neural digressions convolutional

71 - Is Intelligence an Algorithm? With Jade Abbott

ZADevChat Podcast

Play Episode Listen Later Aug 29, 2017 70:49

We chat to Jade Abbott from Retro Rabbit about artificial intelligence, broadly and more specifically about NLP and what that means for us. Chantal, Kenneth & Len talk to Jade about natural language processing, commonly referred to as NLP. What does it take to get a machine to understand what we're saying as people? Jade has always had a fascination with smart machines, from trying to build robots in school and now teaching machines to understand what we're saying. Jade takes a fairly complex topic and helps us come to terms with it. We question whether people, or intelligence, is algorithmic and what that means. Processing natural language is not without challenges and Jade walks us through the maze of terminology and some tools to get started with, and we have several resources below to help as well. What would happen if AI tries to right a movie? What happens if the movie was made? Importantly, neural nets are not the whole of AI. We wander around expert systems, random forests, and other great statistical models that very useful and predictable. Is intelligence just an algorithm? What do you think? Let us know! Find and follow Jade online: * https://twitter.com/alienelf * http://github.com/jaderabbit * https://twitter.com/fmfyband * https://fmfy.bandcamp.com/ Jade has some repos with sample projects on GitHub: * https://github.com/jaderabbit/botcon2016 * https://github.com/jaderabbit/deepdreamsofelectricsheep * https://www.kaggle.com/jaderabbit/training-an-lstm-to-write-songs Jade offers some great resources not specifically covered in the show. For people looking to get into AI: * https://www.coursera.org/learn/machine-learning - Andrew Ng's Coursera Machine Learning course * Kaggle - http://www.kaggle.com/ * https://medium.freecodecamp.org/the-best-data-science-courses-on-the-internet-ranked-by-your-reviews-6dc5b910ea40 How Neural Networks Really Work: * https://www.youtube.com/watch?v=EInQoVLg_UY&t=78s How Neural Network's Really Work by Geoffrey Hinton Using Natural Language for AI * The key blog post on using deep learning for Natural Language Processing: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ * Beautiful tutorial on Word2Vec http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ * My kaggle notebook for training a neural network to generate songs: https://www.kaggle.com/jaderabbit/training-an-lstm-to-write-songs Here are some resources mentioned in the show: * Ex Machina - https://en.wikipedia.org/wiki/Ex_Machina_(film) * Alien Covenant - https://en.wikipedia.org/wiki/Alien:_Covenant * Marvin from Hitchhikers Guide - https://en.wikipedia.org/wiki/Marvin_(character) * Sherlock - https://en.wikipedia.org/wiki/Sherlock_(TV_series) * Natural Language Processing - https://en.wikipedia.org/wiki/Natural_language_processing * word2vec - https://en.wikipedia.org/wiki/Word2vec * Convolutional neural network - https://en.wikipedia.org/wiki/Convolutional_neural_network * Recurrent neural network - https://en.wikipedia.org/wiki/Recurrent_neural_network * Kaggle - https://www.kaggle.com/ * Sunspring | A Sci-Fi Short Film - https://www.youtube.com/watch?v=LY7x2Ihqjmc * Rabbiteer - https://rabbiteer.io/ And finally our picks Jade: * Kaggle - https://www.kaggle.com * Sunspring | A Sci-Fi Short Film - https://www.youtube.com/watch?v=LY7x2Ihqjmc * Creativity: how is AI impacting this human skill? - http://bit.ly/2wjumoD Chantal: * For Computers, Too, It's Hard to Learn to Speak Chinese - http://bit.ly/2graOJr Kenneth: * Westworld - https://en.wikipedia.org/wiki/Westworld_(TV_series) Len: * Instaparse - https://github.com/Engelberg/instaparse Thanks for listening! Stay in touch: * Website & newsletter - https://zadevchat.io * Socialize - https://twitter.com/zadevchat & http://facebook.com/ZADevChat/ * Suggestions and feedback - https://github.com/zadevchat/ping * Subscribe and rate in iTunes - http://bit.ly/zadevchat-itunes

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Convolutional Neural Networks with Matt Zeiler

Machine Learning – Software Engineering Daily

Play Episode Listen Later May 10, 2017 54:37

Convolutional neural networks are a machine learning tool that uses layers of convolution and pooling to process and classify inputs. CNNs are useful for identifying objects in images and video. In this episode, we focus on the application of convolutional neural networks to image and video recognition and classification. Matt Zeiler is the CEO of The post Convolutional Neural Networks with Matt Zeiler appeared first on Software Engineering Daily.

cnn zeiler software engineering daily convolutional convolutional neural networks

181: UNK Reply Bot (higepon)

Rebuild

Play Episode Listen Later May 1, 2017 70:24

Taro Minowa さんをゲストに迎えて、ボット、機械学習、AI などについて話しました。 Show Notes seq2seq の chatbot を日本語で動かしてみた - Higepon’s blog ひげみbot (@higepon_bot) Convolutional neural network Sequence-to-Sequence Models ゼロから作るDeep Learning ―Pythonで学ぶディープラーニングの理論と実装 TensorFlow Keras Theano Chainer 意味分からない。最初からKeras使った方が良くない？流石日本人。Chainer好きすぎでしょ。 MeCab: Yet Another Part-of-Speech and Morphological Analyzer りんな Twitter taught Microsoft’s AI chatbot to be a racist asshole deepmind/sonnet: TensorFlow-based neural network library FaceApp apologizes for building a racist AI Google Photos labeled black people 'gorillas' 新海誠監督の映画から無断使用「Everfilter」をアニメ会社が調査 Is Expensify using Mechanical Turk for reading my receipts? Introducing Echo Look - Hands-Free Camera and Style Assistant Your Samsung TV is eavesdropping on your private conversations Google Home now supports multiple users Google shuts down Burger King's cunning TV ad Facebook is developing a way to read your mind

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169: Your Blog Can Be Generated By Neural Networks (omo)

Rebuild

Play Episode Listen Later Dec 25, 2016 105:57

Hajime Morita さんをゲストに迎えて、達人プログラマーなどについて話しました。 Show Notes Rebuild: Supporter Naoya Ito: "業界の悪習: 新人に10冊も20冊も自分が読んだ本を薦める" 新装版達人プログラマー職人から名匠への道 | Amazon 新装版達人プログラマー職人から名匠への道 | オーム社 eBook Store The Pragmatic Bookshelf Convolutional neural network Rational Unified Process UML 統一モデリング言語 Plantuml レガシーコード改善ガイド Add Code from a Template | Android Studio Protocol Buffers Amazon Athena Sumo Logic Splunk jq Becky! Internet Mail Wanderlust リファクタリング　既存のコードを安全に改善する CODE COMPLETE 第2版上 Error handling and Go Thinking in React - React Design Patterns Martin Fowler UNIXという考え方―その設計思想と哲学 Takuto Wada: "若者への課題図書としてまずは『達人プログラマー』と『UNIXという考え方』を挙げた" 新卒ソフトウェアエンジニアのための技術書100冊 - クックパッド開発者ブログ The Best Software Writing I: Selected and Introduced by Joel Spolsky steps to phantasien

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LM101-059: How to Properly Introduce a Neural Network

network neuroscience networks biological neural computational neural networks perceptron convolutional

Play Episode Listen Later Dec 20, 2016 29:56

I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing a historical perspective on the evolution of the neural network concept from its biological origins. For more details visit us at: www.learningmachines101.com

Episode 107: The Worst of Both Worlds

More Than Just Code podcast - iOS and Swift development, news and advice

Play Episode Listen Later Sep 3, 2016 96:06

We start out discussing the iOS update 9.3.5 which prevents a remote jailbreak of iOS devices, giving bad guys access to users personal data and messages. We also follow up on Touch IC chips popping off iPhones, Star Wars serialization, neural networks, shut down of sync and open sourcing of Vesper, merger of Instapaper with Pinterest and Nest being moved internally into Google. Pro Tip: you may need to clean out your lightning port. We discuss the EU's claim that Apple underpaid on corporate taxes in Ireland. We discuss whether Griffin's Bluetooth adapter for cabled headphones indicates iPhone 7's rumored lack of mini-phone jack. Aaron expounds on his new Car Play enabled car. Picks: Charles Proxy v4, FLEX, Classic CHM youtube channel, How Snapchat’s filters work, Papa John’s Apple TV app, Swift Algorithm Club tutorials. Episode 107 Show Notes: Convolutional neural networks on the iPhone with VGGNet Apple Security Update for iOS Who are the hackers who cracked the iPhone? These are the 25 internet passwords you must not use C4 (conference) Jonathan (Wolf) Rensch The Serial: From Dickens To Star Wars Tony Fadell Brian Hayes MEP on As It Happens (starts at 10:45, end 18:06) iPhone owners sue Apple over 'Touch Disease' Vesper Sync Shutdown Tonight, Open Source Plans Instapaper engineer Brian Donohue is tweeting up a storm reassuring people Google Will Absorb Nest Developers Apple CEO Tim Cook: $14.5 billion EU tax bill has ‘no basis in fact or in law’ A Message to the Apple Community in Europe - Tim Cook Griffin's Bluetooth adapter is early bet on iPhone 7 removing headphone jack Jaybird Sport Meet the Pilot: Smart Earpiece Language Translator CarPlay for Developers The Best Lightning Cable Episode 107 Picks: Charles Proxy v4 FLEX Classic CHM youtube channel How Snapchat’s filters work Papa John’s Apple TV app Swift Algorithm Club: Swift Binary Search Tree Data Structure

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Lecture 13: Introduction to Convolutional Codes

Principles of Digital Communication II

Play Episode Listen Later Jun 16, 2015 81:48

Introduction to Convolutional Codes

lecture codes convolutional

Lecture 14: Introduction to Convolutional Codes

Principles of Digital Communication II

Play Episode Listen Later Jun 16, 2015 82:15

Introduction to Convolutional Codes

lecture codes convolutional

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)

Play Episode Listen Later Jun 8, 2015 32:02

Deep learning machine technology has rapidly developed over the past five years due in part to a variety of actors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called "dropout" in which hidden unit feature detectors are temporarily deleted during the learning process. This article introduces and discusses the concept of "dropout" to support deep learning performance and makes connections of the "dropout" concept to concepts of regularization and model averaging. For more details and background references, check out: www.learningmachines101.com !

LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling

Play Episode Listen Later May 25, 2015 35:59

This podcast discusses talks, papers, and ideas presented at the recent International Conference on Learning Representations 2015 which was followed by the Artificial Intelligence in Statistics 2015 Conference in San Diego. Specifically, commonly used techniques shared by many successful deep learning algorithms such as: rectilinear units, convolutional filters, and max-pooling are discussed. For more details please visit our website at: www.learningmachines101.com!

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LM101-023: How to Build a Deep Learning Machine