1 citations · 1 across the 4 of their papers we have counts for
4 papers
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar +4
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenari…
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
Lei Hsiung, Yung-Chen Tang, Pin-Yu Chen +1
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks. However, a lack of consid…
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…
Learning Geometrically Disentangled Representations of Protein Folding Simulations
N. Joseph Tatro, Payel Das, Pin-Yu Chen +2
Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics. This work focuses on learning a generative…