96 citations · 146 across the 4 of their papers we have counts for
7 papers · 1 filter
Structure Aware Negative Sampling in Knowledge Graphs
Kian Ahrabian, Aarash Feizi, Yasmin Salehi +2
Learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connec…
Adversarial Example Games
Avishek Joey Bose, Gauthier Gidel, Hugo Berard +4
The existence of adversarial examples capable of fooling trained neural network classifiers calls for a much better understanding of possible attacks to guide the development of sa…
Latent Variable Modelling with Hyperbolic Normalizing Flows
Avishek Joey Bose, Ariella Smofsky, Renjie Liao +2
The choice of approximate posterior distributions plays a central role in stochastic variational inference (SVI). One effective solution is the use of normalizing flows \cut{define…
Meta-Graph: Few Shot Link Prediction via Meta Learning
Avishek Joey Bose, Ankit Jain, Piero Molino +1
We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new gra…
Improving Exploration in Soft-Actor-Critic with Normalizing Flows Policies
Patrick Nadeem Ward, Ariella Smofsky, Avishek Joey Bose
Deep Reinforcement Learning (DRL) algorithms for continuous action spaces are known to be brittle toward hyperparameters as well as \cut{being}sample inefficient. Soft Actor Critic…
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling
Avishek Joey Bose, Andre Cianflone, William L. Hamilton
Adversarial attacks on deep neural networks traditionally rely on a constrained optimization paradigm, where an optimization procedure is used to obtain a single adversarial pertur…