287 citations · 658 across the 12 of their papers we have counts for
4 papers · 1 filter
Emergent Complexity via Multi-Agent Competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor +2
Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the…
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda +3
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we…
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
Dung Thai, Shikhar Murty, Trapit Bansal +3
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-t…
RelNet: End-to-End Modeling of Entities & Relations
Trapit Bansal, Arvind Neelakantan, Andrew McCallum
We introduce RelNet: a new model for relational reasoning. RelNet is a memory augmented neural network which models entities as abstract memory slots and is equipped with an additi…