Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP
arXiv.org
Single Headed Attention RNN: Stop Thinking With Your Head
The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth...
Causality for Machine Learning
Bernhard Schölkopf : https://arxiv.org/abs/1911.10500
#MachineLearning #DeepLearning #ArtificialIntelligence
Bernhard Schölkopf : https://arxiv.org/abs/1911.10500
#MachineLearning #DeepLearning #ArtificialIntelligence
arXiv.org
Causality for Machine Learning
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning.
This...
This...
Richard S. Sutton: intelligence is trying to build a model of the world.
Exactly.
https://www.youtube.com/watch?v=XqzT-rBDTPU&fbclid=IwAR3jWkhrWorNDr9Wyvd_zS71O3jrspT3aPzYCJ93ussUPiOnOd7ZRO_EWUc
Exactly.
https://www.youtube.com/watch?v=XqzT-rBDTPU&fbclid=IwAR3jWkhrWorNDr9Wyvd_zS71O3jrspT3aPzYCJ93ussUPiOnOd7ZRO_EWUc
YouTube
The Brains Behind AI: Rich Sutton
Solving the mysteries of machine intelligence could lead to more profound answers about our own human intelligence. Rich Sutton is pioneering the field of reinforcement learning, a type of machine learning that allows machines to learn from interactions with…
This is an exhaustive list of Monte Carlo tree search papers from major conferences including NIPS, ICML, and AAAI. Some of them with publicly available implementations.
https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers
#datascience #machinelearning #deeplearning #python #ai #analytics #datamining
https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers
#datascience #machinelearning #deeplearning #python #ai #analytics #datamining
GitHub
GitHub - benedekrozemberczki/awesome-monte-carlo-tree-search-papers: A curated list of Monte Carlo tree search papers with implementations.
A curated list of Monte Carlo tree search papers with implementations. - GitHub - benedekrozemberczki/awesome-monte-carlo-tree-search-papers: A curated list of Monte Carlo tree search papers with ...
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcem... https://arxiv.org/abs/1911.10334
arXiv.org
Iteratively-Refined Interactive 3D Medical Image Segmentation with...
Existing automatic 3D image segmentation methods usually fail to meet the clinic use. Many studies have explored an interactive strategy to improve the image segmentation performance by...
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