Zobrazeno 1 - 10
of 105 987
pro vyhledávání: '"So-Chen Chen"'
Autor:
Wang, Shuangyi, Lin, Haichuan, Xie, Yiping, Wang, Ziqi, Chen, Dong, Tan, Longyue, Hou, Xilong, Chen, Chen, Zhou, Xiao-Hu, Lin, Shengtao, Pan, Fei, So, Kent Chak-Yu, Hou, Zeng-Guang
Transcatheter tricuspid valve replacement (TTVR) is the latest treatment for tricuspid regurgitation and is in the early stages of clinical adoption. Intelligent robotic approaches are expected to overcome the challenges of surgical manipulation and
Externí odkaz:
http://arxiv.org/abs/2411.12478
Autor:
Chen, Chen
Documents serve as a crucial and indispensable medium for everyday workplace tasks. However, understanding, interacting and creating such documents on today's planar interfaces without any intelligent support are challenging due to our natural cognit
Externí odkaz:
http://arxiv.org/abs/2411.11145
Autor:
Deng, Andong, Chen, Tongjia, Yu, Shoubin, Yang, Taojiannan, Spencer, Lincoln, Tian, Yapeng, Mian, Ajmal Saeed, Bansal, Mohit, Chen, Chen
In this paper, we introduce Motion-Grounded Video Reasoning, a new motion understanding task that requires generating visual answers (video segmentation masks) according to the input question, and hence needs implicit spatiotemporal reasoning and gro
Externí odkaz:
http://arxiv.org/abs/2411.09921
Autor:
Huang, Chao, Xiao, Huichen, Chen, Chen, Chen, Chunyan, Zhao, Yi, Du, Shiyu, Zhang, Yiming, Sha, He, Gu, Ruixin
As the application of large language models in various fields continues to expand, materials science also ushers in opportunities for AI-driven innovation. The traditional way of relying on manual search for materials science-related information is n
Externí odkaz:
http://arxiv.org/abs/2411.08728
The discovery of new materials is very important to the field of materials science. When researchers explore new materials, they often have expected performance requirements for their crystal structure. In recent years, data-driven methods have made
Externí odkaz:
http://arxiv.org/abs/2411.08464
Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking the chemi
Externí odkaz:
http://arxiv.org/abs/2411.08414
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client has both node features and grap
Externí odkaz:
http://arxiv.org/abs/2411.08374
Traditional knowledge distillation focuses on aligning the student's predicted probabilities with both ground-truth labels and the teacher's predicted probabilities. However, the transition to predicted probabilities from logits would obscure certain
Externí odkaz:
http://arxiv.org/abs/2411.08937
Publikováno v:
ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2024
Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annotated datasets, impractical for resource-constrained edge devices. In re
Externí odkaz:
http://arxiv.org/abs/2411.07391