1.8k citations · 1.8k across the 4 of their papers we have counts for
12 papers
Gradient Based Activations for Accurate Bias-Free Learning
Vinod K Kurmi, Rishabh Sharma, Yash Vardhan Sharma +1
Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial lear…
Unsupervised Learning of Compositional Energy Concepts
Yilun Du, Shuang Li, Yash Sharma +2
Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the wea…
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David Klindt, Lukas Schott, Yash Sharma +4
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that represent…
On the Effectiveness of Low Frequency Perturbations
Yash Sharma, Gavin Weiguang Ding, Marcus Brubaker
Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable…
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui +1
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundar…
CAAD 2018: Generating Transferable Adversarial Examples
Yash Sharma, Tien-Dung Le, Moustafa Alzantot
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations carefully crafted to fool the targeted DNN, in both the non-targeted and targeted case. In the non…