activity
20182023
most citedDoes Data Augmentation Lead to Positive Margin?

6 citations · 15 across the 6 of their papers we have counts for

collaborators

9 papers

cs.LG2023

Monotone deep Boltzmann machines

Zhili Feng, Ezra Winston, J. Zico Kolter

Deep Boltzmann machines (DBMs), one of the first ``deep'' learning methods ever studied, are multi-layered probabilistic models governed by a pairwise energy function that describe…

cs.CV2023

Text Descriptions are Compressive and Invariant Representations for Visual Learning

Zhili Feng, Anna Bair, J. Zico Kolter

Modern image classification is based upon directly predicting classes via large discriminative networks, which do not directly contain information about the intuitive visual featur…

cs.LG2021

Non-PSD Matrix Sketching with Applications to Regression and Optimization

Zhili Feng, Fred Roosta, David P. Woodruff

A variety of dimensionality reduction techniques have been applied for computations involving large matrices. The underlying matrix is randomly compressed into a smaller one, while…

cs.LG20212 cited

Provable Adaptation across Multiway Domains via Representation Learning

Zhili Feng, Shaobo Han, Simon S. Du

This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produ…

cs.CL20194 cited

A Structured Learning Approach to Temporal Relation Extraction

Qiang Ning, Zhili Feng, Dan Roth

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on,…

cs.CL20192 cited

Joint Reasoning for Temporal and Causal Relations

Qiang Ning, Zhili Feng, Hao Wu +1

Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and cau…