This is my neck of the woods. - DL
https://www.wbur.org/news/2019/11/25/boston-dynamics-robot-dog-massachusetts-state-police

DeepSynth: Program Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning. https://arxiv.org/abs/1911.10244

SWAG: Item Recommendations using Convolutions on Weighted Graphs. https://arxiv.org/abs/1911.10232

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring. https://arxiv.org/abs/1911.09785

Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP

Causality for Machine Learning
Bernhard Schölkopf : https://arxiv.org/abs/1911.10500
#MachineLearning #DeepLearning #ArtificialIntelligence

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

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

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcem... https://arxiv.org/abs/1911.10334

Compressing Representations for Embedded Deep Learning. https://arxiv.org/abs/1911.10321

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

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