Compressing Representations for Embedded Deep Learning. https://arxiv.org/abs/1911.10321
arXiv.org
Compressing Representations for Embedded Deep Learning
Despite recent advances in architectures for mobile devices, deep learning
computational requirements remains prohibitive for most embedded devices. To
address that issue, we envision sharing the...
computational requirements remains prohibitive for most embedded devices. To
address that issue, we envision sharing the...
PlantDoc: A Dataset for Visual Plant Disease Detection. https://arxiv.org/abs/1911.10317
Deep learning achieved great success in modeling sensory processing. However, such models raise questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (biological circuit) with another (a deep net), without understanding either? https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction https://t.iss.one/ArtificialIntelligenceArticles
Deep learning from the topological, metric, information, causal, physics, computational, and neuroscience perspective. A nice assay by Raul Vicente: "The many faces of deep learning:" https://arxiv.org/abs/1908.10206
Contrastive Learning of Structured World Models
Kipf et al.: https://arxiv.org/abs/1911.12247
Code: https://github.com/tkipf/c-swm
#MachineLearning #DeepLearning #ArtificialIntelligence
Kipf et al.: https://arxiv.org/abs/1911.12247
Code: https://github.com/tkipf/c-swm
#MachineLearning #DeepLearning #ArtificialIntelligence
GitHub
GitHub - tkipf/c-swm: Contrastive Learning of Structured World Models
Contrastive Learning of Structured World Models. Contribute to tkipf/c-swm development by creating an account on GitHub.
Graph Nets library
Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet: https://github.com/deepmind/graph_nets
#ArtificialIntelligence #GraphNetworks #Graphs #DeepLearning #NeuralNetworks #TensorFlow
Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet: https://github.com/deepmind/graph_nets
#ArtificialIntelligence #GraphNetworks #Graphs #DeepLearning #NeuralNetworks #TensorFlow
GitHub
GitHub - google-deepmind/graph_nets: Build Graph Nets in Tensorflow
Build Graph Nets in Tensorflow. Contribute to google-deepmind/graph_nets development by creating an account on GitHub.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
arXiv.org
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often,...
Geoffrey Hinton: "Does the Brain do Inverse Graphics?"
https://www.youtube.com/watch?v=TFIMqt0yT2I
https://www.youtube.com/watch?v=TFIMqt0yT2I
YouTube
Geoffrey Hinton: "Does the Brain do Inverse Graphics?"
Graduate Summer School 2012: Deep Learning, Feature Learning
"Does the Brain do Inverse Graphics?"
Geoffrey Hinton, University of Toronto
Institute for Pure and Applied Mathematics, UCLA
July 12, 2012
For more information: https://www.ipam.ucla.edu/programs/summer…
"Does the Brain do Inverse Graphics?"
Geoffrey Hinton, University of Toronto
Institute for Pure and Applied Mathematics, UCLA
July 12, 2012
For more information: https://www.ipam.ucla.edu/programs/summer…
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Bengio et al.: https://arxiv.org/abs/1901.10912
#MetaTransfer #CausalMechanisms #ArtificialIntelligence
Bengio et al.: https://arxiv.org/abs/1901.10912
#MetaTransfer #CausalMechanisms #ArtificialIntelligence
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#CausalModels #MachineLearning #ArtificialIntelligence
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#CausalModels #MachineLearning #ArtificialIntelligence
arXiv.org
Learning Neural Causal Models from Unknown Interventions
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical...
Handbook of Graphical Models
Marloes Maathuis, Mathias Drton, Steen Lauritzen and Martin Wainwright : https://stat.ethz.ch/~maathuis/papers/Handbook.pdf
#Handbook #GraphicalModels
Marloes Maathuis, Mathias Drton, Steen Lauritzen and Martin Wainwright : https://stat.ethz.ch/~maathuis/papers/Handbook.pdf
#Handbook #GraphicalModels
Causal Inference: What If
Miguel A. Hernán, James M. Robins : https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2019/11/ci_hernanrobins_10nov19.pdf
#CausalInference
Miguel A. Hernán, James M. Robins : https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2019/11/ci_hernanrobins_10nov19.pdf
#CausalInference
Breast Histopathology Images Dataset
Download: https://www.kaggle.com/paultimothymooney/breast-histopathology-images
Download: https://www.kaggle.com/paultimothymooney/breast-histopathology-images
Kaggle
Breast Histopathology Images
198,738 IDC(-) image patches; 78,786 IDC(+) image patches
A tutorial to implement state-of-the-art NLP models with Fastai for Sentiment Analysis
Maximilien Roberti : https://towardsdatascience.com/fastai-with-transformers-bert-roberta-xlnet-xlm-distilbert-4f41ee18ecb2
#FastAI #NLP #Transformers
Maximilien Roberti : https://towardsdatascience.com/fastai-with-transformers-bert-roberta-xlnet-xlm-distilbert-4f41ee18ecb2
#FastAI #NLP #Transformers
Pre-Debate Material :
Recurrent Independent Mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf : https://arxiv.org/abs/1909.10893
#MachineLearning #Generalization #ArtificialIntelligence
Recurrent Independent Mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf : https://arxiv.org/abs/1909.10893
#MachineLearning #Generalization #ArtificialIntelligence
arXiv.org
Recurrent Independent Mechanisms
Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes. We propose...
Pre-Debate Material :
Meta transfer learning for factorizing representations and knowledge for AI - Yoshua Bengio : https://youtu.be/CHnJYBpMjNY
#AIDebate #MontrealAI
Meta transfer learning for factorizing representations and knowledge for AI - Yoshua Bengio : https://youtu.be/CHnJYBpMjNY
#AIDebate #MontrealAI
YouTube
Meta transfer learning for factorizing representations and knowledge for AI - Yoshua Bengio
Speaker: Yoshua Bengio
Title: Meta transfer learning for factorizing representations and knowledge for AI
Abstract:
Whereas machine learning theory has focused on generalization to examples from the same distribution as the training data, better understanding…
Title: Meta transfer learning for factorizing representations and knowledge for AI
Abstract:
Whereas machine learning theory has focused on generalization to examples from the same distribution as the training data, better understanding…