107 citations · 228 across the 23 of their papers we have counts for
5 papers · 1 filter
When MAML Can Adapt Fast and How to Assist When It Cannot
Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha
Model-Agnostic Meta-Learning (MAML) and its variants have achieved success in meta-learning tasks on many datasets and settings. On the other hand, we have just started to understa…
Topic Augmented Generator for Abstractive Summarization
Melissa Ailem, Bowen Zhang, Fei Sha
Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summa…
Neural Theorem Provers Do Not Learn Rules Without Exploration
Michiel de Jong, Fei Sha
Neural symbolic processing aims to combine the generalization of logical learning approaches and the performance of neural networks. The Neural Theorem Proving (NTP) model by Rockt…
Amortized Inference of Variational Bounds for Learning Noisy-OR
Yiming Yan, Melissa Ailem, Fei Sha
Classical approaches for approximate inference depend on cleverly designed variational distributions and bounds. Modern approaches employ amortized variational inference, which use…
Hyper-parameter Tuning under a Budget Constraint
Zhiyun Lu, Chao-Kai Chiang, Fei Sha
We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint. We propose to solve it as a sequential decision making pro…