Zobrazeno 1 - 10
of 22 927
pro vyhledávání: '"HUANG, Xin"'
We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offe
Externí odkaz:
http://arxiv.org/abs/2411.15138
We study fair division of indivisible chores among $n$ agents with additive cost functions using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist for more than two agents, the goal has been to improve its
Externí odkaz:
http://arxiv.org/abs/2411.04391
This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, t
Externí odkaz:
http://arxiv.org/abs/2410.17617
This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and accurate risk assessment. The
Externí odkaz:
http://arxiv.org/abs/2410.12642
Autor:
Zhou, Hao, Wang, Zhijun, Huang, Shujian, Huang, Xin, Han, Xue, Feng, Junlan, Deng, Chao, Luo, Weihua, Chen, Jiajun
Large Language Models (LLMs) are often English-centric due to the disproportionate distribution of languages in their pre-training data. Enhancing non-English language capabilities through post-pretraining often results in catastrophic forgetting of
Externí odkaz:
http://arxiv.org/abs/2408.11396
Autor:
Aziz, Haris, Huang, Xin, Kimura, Kei, Saha, Indrajit, Sun, Zhaohong, Suzuki, Mashbat, Yokoo, Makoto
We consider the problem of fair allocation of indivisible items with subsidies when agents have weighted entitlements. After highlighting several important differences from the unweighted case, we present several results concerning weighted envy-free
Externí odkaz:
http://arxiv.org/abs/2408.08711
Multimodal Large Language Models (MLLMs) excel in solving text-based mathematical problems, but they struggle with mathematical diagrams since they are primarily trained on natural scene images. For humans, visual aids generally enhance problem-solvi
Externí odkaz:
http://arxiv.org/abs/2408.08640
Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain e
Externí odkaz:
http://arxiv.org/abs/2408.06082
Unified graph representation learning aims to produce node embeddings, which can be applied to multiple downstream applications. However, existing studies based on graph neural networks and language models either suffer from the limitations of numero
Externí odkaz:
http://arxiv.org/abs/2408.05456
Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of a user are inherently complex and high-dimensional, while the observe
Externí odkaz:
http://arxiv.org/abs/2407.17802