96 citations · 205 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
Inductive Relation Prediction by Subgraph Reasoning
Komal K. Teru, Etienne Denis, William L. Hamilton
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, t…
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nod…
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
Koustuv Sinha, Shagun Sodhani, Jin Dong +2
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust wa…
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia
Charles C. Onu, Jonathan Lebensold, William L. Hamilton +1
Despite continuing medical advances, the rate of newborn morbidity and mortality globally remains high, with over 6 million casualties every year. The prediction of pathologies aff…
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…