🚀 Deep Learning Paper of the Week
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
MLT is excited to support and be part of the CTDS Kaggle competition organized by Sanyam Bhutani, Chai Time Data Science | CTDS.Show. The Chai Time Data Science Show is a podcast, video and blog based show for interviews with ML Practitioners, Kagglers, Research Scientists and all things Data Science. It's a continuation of the … Continue reading CTDS KAGGLE COMPETITION LAUNCH
DEEP LEARNING PAPER OF THE WEEK | CVPR 2020 BEST PAPER AWARD