KPMG IGNITION TOKYO、MLTとのパートナーシップを締結

KPMG Ignition Tokyoは、オープンエデュケーション、オープンソース、オープンサイエンスを通じた機械学習と人工知能の民主化に取り組む一般社団法人Machine Learning Tokyo とパートナーシップ契約を締結しました。MLTとのパートナーシップを通じて、テクノロジーコミュニティのさらなる成長と発展に貢献することを目指しています。

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

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