2 code implementations • NeurIPS 2021 • Devendra Singh Sachan, Siva Reddy, William Hamilton, Chris Dyer, Dani Yogatama
We model retrieval decisions as latent variables over sets of relevant documents.
no code implementations • 22 Oct 2020 • Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan, William Hamilton
In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text.
1 code implementation • EACL 2021 • Devendra Singh Sachan, Yuhao Zhang, Peng Qi, William Hamilton
Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks.
1 code implementation • ICLR 2021 • Lu Liu, William Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle
We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources.
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Few-Shot Image Classification
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no code implementations • 14 Oct 2019 • Jonathan Lebensold, William Hamilton, Borja Balle, Doina Precup
Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by leveraging information (e. g., via pre-training) on other related tasks.