6 papers
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
Zhichen Zeng, Ruizhong Qiu, Wenxuan Bao +6
Graph neural networks, despite their impressive performance, are highly vulnerable to distribution shifts on graphs. Existing graph domain adaptation (graph DA) methods often impli…
Agentic Reasoning for Large Language Models
Tianxin Wei, Ting-Wei Li, Zhining Liu +26
Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilitie…
Subspace Alignment for Vision-Language Model Test-time Adaptation
Zhichen Zeng, Wenxuan Bao, Xiao Lin +8
Vision-language models (VLMs), despite their extraordinary zero-shot capabilities, are vulnerable to distribution shifts. Test-time adaptation (TTA) emerges as a predominant strate…
DGL-RSIS: Decoupling Global Spatial Context and Local Class Semantics for Training-Free Remote Sensing Image Segmentation
Boyi Li, Ce Zhang, Richard M. Timmerman +1
The emergence of vision language models (VLMs) bridges the gap between vision and language, enabling multimodal understanding beyond traditional visual-only deep learning models. H…
Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning
Wenxuan Bao, Ruxi Deng, Ruizhong Qiu +3
Test-time adaptation with pre-trained vision-language models has gained increasing attention for addressing distribution shifts during testing. Among these approaches, memory-based…
Ask, and it shall be given: On the Turing completeness of prompting
Ruizhong Qiu, Zhe Xu, Wenxuan Bao +1
Since the success of GPT, large language models (LLMs) have been revolutionizing machine learning and have initiated the so-called LLM prompting paradigm. In the era of LLMs, peopl…