12 citations · 22 across the 7 of their papers we have counts for
7 papers
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…
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…
Are Multimodal Transformers Robust to Missing Modality?
Mengmeng Ma, Jian Ren, Long Zhao +2
Multimodal data collected from the real world are often imperfect due to missing modalities. Therefore multimodal models that are robust against modal-incomplete data are highly pr…
SMIL: Multimodal Learning with Severely Missing Modality
Mengmeng Ma, Jian Ren, Long Zhao +3
A common assumption in multimodal learning is the completeness of training data, i.e., full modalities are available in all training examples. Although there exists research endeav…