297 citations · 515 across the 18 of their papers we have counts for
4 papers · 1 filter
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces
Volodymyr Kuleshov, Evgenii Nikishin, Shantanu Thakoor +2
Modern classification algorithms are susceptible to adversarial examples--perturbations to inputs that cause the algorithm to produce undesirable behavior. In this work, we seek to…
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
Large-Scale Representation Learning on Graphs via Bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar +5
Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-…
Geometric Entropic Exploration
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade +7
Exploration is essential for solving complex Reinforcement Learning (RL) tasks. Maximum State-Visitation Entropy (MSVE) formulates the exploration problem as a well-defined policy…