20 citations · 20 across the 1 of their papers we have counts for
3 papers
cs.DC2021★ 20 cited
Maximizing Parallelism in Distributed Training for Huge Neural Networks
Zhengda Bian, Qifan Xu, Boxiang Wang +1
The recent Natural Language Processing techniques have been refreshing the state-of-the-art performance at an incredible speed. Training huge language models is therefore an impera…
cs.LG2021
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
Tong Wang, Jingyi Yang, Yunyi Li +1
We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE predict…
stat.ML2019
Sparse Tensor Additive Regression
Botao Hao, Boxiang Wang, Pengyuan Wang +3
Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that model…