Yann LeCun and a team of researchers propose Barlow Twins, a method that learns self-supervised representations through a joint embedding of distorted images, with an objective function that can make the embedding vectors almost identical while reducing redundancy between their components.
A team from Max-Planck Institute for Intelligent Systems, ETH Zurich, Google Research Amsterdam, Mila and the University of Montreal make an effort to bring together causality and machine learning research programs, delineate implications of causality for machine learning and propose critical areas for future research.
University of Toronto researchers propose a BERT-inspired training approach as a self-supervised pretraining step to enable deep neural networks to leverage newly and publicly available massive EEG (electroencephalography) datasets for downstream brain-computer-interface (BCI) applications.
Stanford researchers’ DERL (Deep Evolutionary Reinforcement Learning) is a novel computational framework that enables AI agents to evolve morphologies and learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information.
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.