13 citations · 21 across the 6 of their papers we have counts for
5 papers · 1 filter
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra +2
Recent success in fine-tuning large models, that are pretrained on broad data at scale, on downstream tasks has led to a significant paradigm shift in deep learning, from task-cent…
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
Ching-Yun Ko, Jeet Mohapatra, Sijia Liu +3
As a seminal tool in self-supervised representation learning, contrastive learning has gained unprecedented attention in recent years. In essence, contrastive learning aims to leve…
Higher-Order Certification for Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng +3
Randomized smoothing is a recently proposed defense against adversarial attacks that has achieved SOTA provable robustness against perturbations. A number of publications…
Hidden Cost of Randomized Smoothing
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei +4
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public. While immense interests were in either crafting adve…
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Jeet Mohapatra, Tsui-Wei, Weng +3
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the -norm t…