33 citations · 54 across the 12 of their papers we have counts for
12 papers
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach
Weiyi Gong, Tao Sun, Hexin Bai +3
Predicting electronic band structures from crystal structures is crucial for understanding structure-property correlations in materials science. First-principles approaches are acc…
Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations
Lu Pang, Tao Sun, Weimin Lyu +2
Recently, backdoor attack has become an increasing security threat to deep neural networks and drawn the attention of researchers. Backdoor attacks exploit vulnerabilities in third…
Backdooring Vision-Language Models with Out-Of-Distribution Data
Weimin Lyu, Jiachen Yao, Saumya Gupta +6
The emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descr…
Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder
Tao Sun, Lu Pang, Weimin Lyu +2
Deep neural networks are vulnerable to backdoor attacks, where an adversary manipulates the model behavior through overlaying images with special triggers. Existing backdoor defens…
Backdoor Cleansing with Unlabeled Data
Lu Pang, Tao Sun, Haibin Ling +1
Due to the increasing computational demand of Deep Neural Networks (DNNs), companies and organizations have begun to outsource the training process. However, the externally trained…
Local Context-Aware Active Domain Adaptation
Tao Sun, Cheng Lu, Haibin Ling
Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context o…
Domain Adaptation with Adversarial Training on Penultimate Activations
Tao Sun, Cheng Lu, Haibin Ling
Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimat…
Prior Knowledge Guided Unsupervised Domain Adaptation
Tao Sun, Cheng Lu, Haibin Ling
The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an attractive technique in many real-world applications, though it also brings great challenges…
Incorporation of density scaling constraint in density functional design via contrastive representation learning
Weiyi Gong, Tao Sun, Hexin Bai +7
In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (…
Safe Self-Refinement for Transformer-based Domain Adaptation
Tao Sun, Cheng Lu, Tianshuo Zhang +1
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a…
Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications
Gautham Ramajayam, Tao Sun, Chiu C. Tan +2
Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information th…
End-to-end Full Projector Compensation
Bingyao Huang, Tao Sun, Haibin Ling
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface. Traditional methods usual…