2 citations · 7 across the 6 of their papers we have counts for
6 papers
One step closer to unbiased aleatoric uncertainty estimation
Wang Zhang, Ziwen Ma, Subhro Das +4
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties…
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Wang Zhang, Tsui-Wei Weng, Subhro Das +3
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws. This pape…
Certified Interpretability Robustness for Class Activation Mapping
Alex Gu, Tsui-Wei Weng, Pin-Yu Chen +2
Interpreting machine learning models is challenging but crucial for ensuring the safety of deep networks in autonomous driving systems. Due to the prevalence of deep learning based…
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 from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Zhengqi Gao, Fan-Keng Sun, Mingran Yang +7
Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-worl…
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…