1 citations · 2 across the 15 of their papers we have counts for
17 papers
Task-Relevant Null-Space Residuals for Non-Injective Neural Mappings
Bizu Feng, Zhimu Yang, Shuming Wang +4
Non-injective mappings in neural networks map distinct inputs to the same representation, thereby implicitly inducing equivalence relations in the input space. However, the input d…
A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs
Bizu Feng, Zhimu Yang, Shuming Wang +4
We study feature-level and node-level explanations for graph neural networks (GNNs) through the lens of Aumann-Shapley attribution. Path-integral methods such as Integrated Gradien…
Monkey King Bang: A Unified Scientific Multimodal Foundation Model
Hesen Chen, Xinyu Su, Xiaomeng Yang +11
Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition. Existing systems are either specia…
Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents
Yunhui Gan, Tan Pan, Kaiyu Guo +5
Medical AI agents increasingly use external tools for diagnosis, treatment recommendation, and evidence retrieval, yet most existing approaches assume that task-appropriate tools a…
FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction
Qi Si, Penglei Wang, Yushuai Wu +5
Predicting spatial gene expression from routine H\&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essenti…
Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
Tan Pan, Shuhao Mei, Yixuan Sun +8
Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or m…
Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
Xigui Li, Hongwei Zhang, Ruoxi Jiang +6
Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer…
Structure-based RNA Design by Step-wise Optimization of Latent Diffusion Model
Qi Si, Xuyang Liu, Penglei Wang +3
RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence…
Tracing the Heart's Pathways: ECG Representation Learning from a Cardiac Conduction Perspective
Tan Pan, Yixuan Sun, Chen Jiang +8
The multi-lead electrocardiogram (ECG) stands as a cornerstone of cardiac diagnosis. Recent strides in electrocardiogram self-supervised learning (eSSL) have brightened prospects f…
Rethinking Intracranial Aneurysm Vessel Segmentation: A Perspective from Computational Fluid Dynamics Applications
Feiyang Xiao, Yichi Zhang, Xigui Li +7
The precise segmentation of intracranial aneurysms and their parent vessels (IA-Vessel) is a critical step for hemodynamic analyses, which mainly depends on computational fluid dyn…
Adaptive Reasoning Executor: A Collaborative Agent System for Efficient Reasoning
Zehui Ling, Deshu Chen, Yichi Zhang +4
Recent advances in Large Language Models (LLMs) demonstrate that chain-of-thought prompting and deep reasoning substantially enhance performance on complex tasks, and multi-agent s…
Minimal Semantic Sufficiency Meets Unsupervised Domain Generalization
Tan Pan, Kaiyu Guo, Dongli Xu +8
The generalization ability of deep learning has been extensively studied in supervised settings, yet it remains less explored in unsupervised scenarios. Recently, the Unsupervised…
Exploiting Layer Normalization Fine-tuning in Visual Transformer Foundation Models for Classification
Zhaorui Tan, Tan Pan, Kaizhu Huang +8
LayerNorm is pivotal in Vision Transformers (ViTs), yet its fine-tuning dynamics under data scarcity and domain shifts remain underexplored. This paper shows that shifts in LayerNo…
PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
Changchun Yang, Haoyang Li, Yushuai Wu +8
While pathology foundation models have transformed cancer image analysis, they often lack integration with molecular data at single-cell resolution, limiting their utility for prec…
Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning
Tan Pan, Zhaorui Tan, Kaiyu Guo +6
3D medical image self-supervised learning (mSSL) holds great promise for medical analysis. Effectively supporting broader applications requires considering anatomical structure var…
Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty
Zehui Ling, Deshu Chen, Hongwei Zhang +3
Large language models (LLMs) have demonstrated significant advancements in reasoning capabilities, performing well on various challenging benchmarks. Techniques like Chain-of-Thoug…
Efficient Network Automatic Relevance Determination
Hongwei Zhang, Ziqi Ye, Xinyuan Wang +5
We propose Network Automatic Relevance Determination (NARD), an extension of ARD for linearly probabilistic models, to simultaneously model sparse relationships between inputs $X \…