44 citations · 47 across the 5 of their papers we have counts for
10 papers
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…
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
Rui Lin, Ching-Yun Ko, Zhuolun He +5
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitut…
Fastened CROWN: Tightened Neural Network Robustness Certificates
Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong +3
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reli…