29 citations · 62 across the 27 of their papers we have counts for
7 papers · 1 filter
Defining and Evaluating Physical Safety for Large Language Models
Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho
Large Language Models (LLMs) are increasingly used to control robotic systems such as drones, but their risks of causing physical threats and harm in real-world applications remain…
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Ming-Yu Chung, Sheng-Yen Chou, Chia-Mu Yu +3
Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original tr…
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
Hao-Lun Sun, Lei Hsiung, Nandhini Chandramoorthy +2
Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high. An effective strategy for reducing such consumpti…
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…
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…
Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration
Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho
Neural network calibration is an essential task in deep learning to ensure consistency between the confidence of model prediction and the true correctness likelihood. In this paper…
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
Zhiyuan He, Yijun Yang, Pin-Yu Chen +2
Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs' vulnerability to Adversarial Examples (AE) hinders their deployments to safety-cri…