96 citations · 205 across the 5 of their papers we have counts for
13 papers
Learning an Unreferenced Metric for Online Dialogue Evaluation
Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang +3
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to devel…
Evaluating Logical Generalization in Graph Neural Networks
Koustuv Sinha, Shagun Sodhani, Joelle Pineau +1
Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner. However, while relatio…
Towards Graph Representation Learning in Emergent Communication
Agnieszka Słowik, Abhinav Gupta, William L. Hamilton +2
Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we…
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…
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…