2 citations · 7 across the 8 of their papers we have counts for
9 papers
Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts
Mengmeng Ma, Yunxiang Peng, Tang Li +4
Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models "…
Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers
Tang Li, Yanlin Chen, Mengmeng Ma +1
Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment. Sparse autoe…
SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey
Kien X. Nguyen, Fengchun Qiao, Arthur Trembanis +1
A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have be…
DEAL: Disentangle and Localize Concept-level Explanations for VLMs
Tang Li, Mengmeng Ma, Xi Peng
Large pre-trained Vision-Language Models (VLMs) have become ubiquitous foundational components of other models and downstream tasks. Although powerful, our empirical results reveal…
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
Mengmeng Ma, Tang Li, Xi Peng
Federated Learning is widely employed to tackle distributed sensitive data. Existing methods primarily focus on addressing in-federation data heterogeneity. However, we observed th…
Adaptive Cascading Network for Continual Test-Time Adaptation
Kien X. Nguyen, Fengchun Qiao, Xi Peng
We study the problem of continual test-time adaption where the goal is to adapt a source pre-trained model to a sequence of unlabelled target domains at test time. Existing methods…
Topology-aware Robust Optimization for Out-of-distribution Generalization
Fengchun Qiao, Xi Peng
Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications. Existing methods suffer from overly pessimi…
Are Data-driven Explanations Robust against Out-of-distribution Data?
Tang Li, Fengchun Qiao, Mengmeng Ma +1
As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantl…
Deep Learning for Spatiotemporal Modeling of Urbanization
Tang Li, Jing Gao, Xi Peng
Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effecti…