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Data Augmentation for Meta-Learning
Conventional image classifiers are trained by randomly sampling mini-bat...
10/14/2020 ∙ by Renkun Ni, et al. ∙
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Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Generative models are increasingly able to produce remarkably high quali...
10/13/2020 ∙ by Liam Fowl, et al. ∙
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Active Imitation Learning with Noisy Guidance
Imitation learning algorithms provide state-of-the-art results on many s...
05/26/2020 ∙ by Kianté Brantley, et al. ∙
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Meta-Learning for Few-Shot NMT Adaptation
We present META-MT, a meta-learning approach to adapt Neural Machine Tra...
04/06/2020 ∙ by Amr Sharaf, et al. ∙
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Meta-Learning for Contextual Bandit Exploration
We describe MELEE, a meta-learning algorithm for learning a good explora...
01/23/2019 ∙ by Amr Sharaf, et al. ∙
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Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
In the slot-filling paradigm, where a user can refer back to slots in th...
11/27/2018 ∙ by Amr Sharaf, et al. ∙
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The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
We describe the University of Maryland machine translation systems submi...
08/03/2017 ∙ by Amr Sharaf, et al. ∙
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