10 citations · 19 across the 4 of their papers we have counts for
8 papers
On Hard Episodes in Meta-Learning
Samyadeep Basu, Amr Sharaf, Nicolo Fusi +1
Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes. Different episodes, however, may vary in hardness and quality leading to a w…
Semi-Supervised Few-Shot Intent Classification and Slot Filling
Samyadeep Basu, Karine lp Kiun Chong, Amr Sharaf +7
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data t…
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Liam Fowl, Micah Goldblum, Arjun Gupta +2
Generative models are increasingly able to produce remarkably high quality images and text. The community has developed numerous evaluation metrics for comparing generative models.…
Active Imitation Learning with Noisy Guidance
Kianté Brantley, Amr Sharaf, Hal Daumé
Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies. Such algorithms assume training-time ac…
Meta-Learning for Few-Shot NMT Adaptation
Amr Sharaf, Hany Hassan, Hal Daumé
We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adap…
Meta-Learning for Contextual Bandit Exploration
Amr Sharaf, Hal Daumé
We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting. Here, an algorithm must take actions based on cont…