39 citations · 107 across the 9 of their papers we have counts for
17 papers
Demystifying and Generalizing BinaryConnect
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari +2
BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite…
Quantifying and Improving Transferability in Domain Generalization
Guojun Zhang, Han Zhao, Yaoliang Yu +1
Out-of-distribution generalization is one of the key challenges when transferring a model from the lab to the real world. Existing efforts mostly focus on building invariant featur…
Posterior Differential Regularization with f-divergence for Improving Model Robustness
Hao Cheng, Xiaodong Liu, Lis Pereira +2
We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and nois…
OLALA: Object-Level Active Learning for Efficient Document Layout Annotation
Zejiang Shen, Jian Zhao, Melissa Dell +2
Document images often have intricate layout structures, with numerous content regions (e.g. texts, figures, tables) densely arranged on each page. This makes the manual annotation…
Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to "small, imperceptible" perturbations known as adversarial attacks. While the majority of ex…
Density Deconvolution with Normalizing Flows
Tim Dockhorn, James A. Ritchie, Yaoliang Yu +1
Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples. We can fit a Gaussian mixture model to the underlying density by…