3.1k citations · 6.9k across the 91 of their papers we have counts for
11 papers · 1 filter
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Yihong Chen, Pushkar Mishra, Luca Franceschi +3
Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs). Howe…
Concept Matching for Low-Resource Classification
Federico Errica, Ludovic Denoyer, Bora Edizel +4
We propose a model to tackle classification tasks in the presence of very little training data. To this aim, we approximate the notion of exact match with a theoretically sound mec…
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +2
Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment cl…
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel +2
Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, a…
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
Viswanath Sivakumar, Olivier Delalleau, Tim Rocktäschel +6
Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-c…
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
Alexander I. Cowen-Rivers, Pasquale Minervini, Tim Rocktaschel +3
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional varia…