133 citations · 246 across the 12 of their papers we have counts for
13 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…
SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition
Jingxiao Yang, DaLin He, Miao Pan +7
Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains ch…
Interpretable Failure Detection with Human-Level Concepts
Kien X. Nguyen, Tang Li, Xi Peng
Reliable failure detection holds paramount importance in safety-critical applications. Yet, neural networks are known to produce overconfident predictions for misclassified samples…
Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality
Qitong Wang, Tang Li, Kien X. Nguyen +1
Vision-Language Models (VLMs), such as CLIP, have already seen widespread applications. Researchers actively engage in further fine-tuning VLMs in safety-critical domains. In these…
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales
Tang Li, Mengmeng Ma, Xi Peng
Large pretrained foundation models demonstrate exceptional performance and, in some high-stakes applications, even surpass human experts. However, most of these models are currentl…
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…
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…
Search for gravitational waves from Scorpius X-1 with a hidden Markov model in O3 LIGO data
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1669
Results are presented for a semi-coherent search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1, using a hidden Markov model (HMM) to allow for spin…
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…
Tests of General Relativity with GWTC-3
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1682
The ever-increasing number of detections of gravitational waves (GWs) from compact binaries by the Advanced LIGO and Advanced Virgo detectors allows us to perform ever-more sensiti…
Search for subsolar-mass binaries in the first half of Advanced LIGO and Virgo's third observing run
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1634
We report on a search for compact binary coalescences where at least one binary component has a mass between 0.2 and 1.0 in Advanced LIGO and Advanced Virgo dat…