7 papers
Geometric Collapse: When Vision Models Fail to Verify Physical Causality
Wentao Zhang, Jinhu Qi, Weiqiang Jin +3
Recent progress in large-scale self-supervised learning has improved dense geometric prediction, but it remains unclear whether such scaling yields inference-time physical plausibi…
A Survey on Vision-Language-Action Models for Embodied AI
Yueen Ma, Zixing Song, Yuzheng Zhuang +2
Embodied AI is widely recognized as a cornerstone of artificial general intelligence (AGI) because it involves controlling embodied agents to perform tasks in the physical world. B…
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Dianzhi Yu, Xinni Zhang, Yankai Chen +4
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As models…
RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
Chunyu Miao, Henry Peng Zou, Yangning Li +28
Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing…
Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning
Hung-Chieh Fang, Hsuan-Tien Lin, Irwin King +1
Federated Unsupervised Learning (FUL) aims to learn expressive representations in federated and self-supervised settings. The quality of representations learned in FUL is usually d…
Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering
Yankai Chen, Yue Que, Xinni Zhang +2
Learning vectorized embeddings is fundamental to many recommender systems for user-item matching. To enable efficient online inference, representation binarization, which embeds la…