AI, Machine Learning and Deep Learning this month, at a glance.
🚀 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
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
In this online RL series we will cover "Reinforcement learning: An introduction" by Richard Sutton and Andrew Barto.
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"
By Alisher Abdulkhaev and Suzana Ilić Issue #9: March 2020 Keras turned 5Facebook releases SynSinTuring Award 2019 winners: Patrick M. Hanrahan and Edwin E. CatmullNeRF: Representing Scenes as Neural Radiance Fields for View SynthesisStanford NLP Group releases StanzaGoogle announced TensorFlow Developer CertificateICLR 2020 as a fully virtual conference due to the coronavirus spreadingGoogle announced TensorFlow Quantum: … Continue reading AI DIGEST #MARCH20