June 30, 2020June 30, 2020 MACHINE LEARNING TOKYO

AI DIGEST #JUNE20

AI, Machine Learning and Deep Learning this month, at a glance.

June 26, 2020 MACHINE LEARNING TOKYO

DLPOTW: DISCOVERING SYMBOLIC MODELS FROM DEEP LEARNING WITH INDUCTIVE BIASES

🚀 DEEP LEARNING PAPER OF THE WEEK Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho Abstract "We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The technique works as follows: we first encourage sparse latent representations when we train … Continue reading DLPOTW: DISCOVERING SYMBOLIC MODELS FROM DEEP LEARNING WITH INDUCTIVE BIASES

June 25, 2020June 25, 2020 MACHINE LEARNING TOKYO

PAPERS WITH ANNOTATIONS

MLT Co-Director Alisher Abdulkhaev shares his Papers with Annotations. This project compiles multiple (AI related) papers with illustrations, annotations, and brief explanations of technical keywords, terms and previous studies which makes it easy to read the paper and follow the main idea. Object detection papers with annotationsAI and Cognitive Science related papers with annotations Please … Continue reading PAPERS WITH ANNOTATIONS

May 14, 2020May 16, 2020 MACHINE LEARNING TOKYO

RL READING & DISCUSSION SESSIONS

In this online RL series we will cover "Reinforcement learning: An introduction" by Richard Sutton and Andrew Barto.

April 6, 2020 MACHINE LEARNING TOKYO

CNN ARCHITECTURES: XCEPTION

Extract the most important information from a CNN paper and learn how to code up the architecture. This time: "Xception: Deep Learning with Depthwise Separable Convolutions"