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GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho
Current studies on adversarial robustness mainly focus on aggregating local robustness results from a set of data samples to evaluate and rank different models. However, the local…
Evaluating the Adversarial Robustness for Fourier Neural Operators
Abolaji D. Adesoji, Pin-Yu Chen
In recent years, Machine-Learning (ML)-driven approaches have been widely used in scientific discovery domains. Among them, the Fourier Neural Operator (FNO) was the first to simul…
Auto-Transfer: Learning to Route Transferrable Representations
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss +3
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be diffic…
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Shuai Zhang, Meng Wang, Sijia Liu +2
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes,…
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…
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen +2
Efficient model selection for identifying a suitable pre-trained neural network to a downstream task is a fundamental yet challenging task in deep learning. Current practice requir…