2.7k citations · 6.8k across the 64 of their papers we have counts for
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e-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz +1
In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from…
Learning with Stochastic Guidance for Navigation
Linhai Xie, Yishu Miao, Sen Wang +5
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of hig…
Sentence Encoding with Tree-constrained Relation Networks
Lei Yu, Cyprien de Masson d'Autume, Chris Dyer +3
The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variant…
Learning to Discover, Ground and Use Words with Segmental Neural Language Models
Kazuya Kawakami, Chris Dyer, Phil Blunsom
We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented c…
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu +3
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. Howe…
Neural Arithmetic Logic Units
Andrew Trask, Felix Hill, Scott Reed +3
Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training. T…
Encoding Spatial Relations from Natural Language
Tiago Ramalho, Tomáš Kočiský, Frederic Besse +5
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fa…
Pushing the bounds of dropout
Gábor Melis, Charles Blundell, Tomáš Kočiský +3
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the o…