8 citations · 10 across the 10 of their papers we have counts for
11 papers
PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening
Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang +2
Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP and its recent extensions s…
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Ziming Wang, Yinghua Yao, Changwu Huang +2
Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully…
Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Yinghua Yao, Yuangang Pan, Xixian Chen
Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approach…
Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization
Yuanyuan Chang, Yinghua Yao, Tao Qin +3
Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. Howe…
Alpha and Prejudice: Improving -sized Worst-case Fairness via Intrinsic Reweighting
Jing Li, Yinghua Yao, Yuangang Pan +3
Worst-case fairness with off-the-shelf demographics achieves group parity by maximizing the model utility of the worst-off group. Nevertheless, demographic information is often una…
PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation
Yinghua Yao, Yuangang Pan, Jing Li +2
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these proper…
Sanitized Clustering against Confounding Bias
Yinghua Yao, Yuangang Pan, Jing Li +2
Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding…
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack
Dashan Gao, Yunce Zhao, Yinghua Yao +3
Deep learning models can be fooled by small -norm adversarial perturbations and natural perturbations in terms of attributes. Although the robustness against each perturbation…
Earning Extra Performance from Restrictive Feedbacks
Jing Li, Yuangang Pan, Yueming Lyu +3
Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of loc…
TRIP: Refining Image-to-Image Translation via Rival Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
Relative attribute (RA), referring to the preference over two images on the strength of a specific attribute, can enable fine-grained image-to-image translation due to its rich sem…
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset posse…