Zobrazeno 1 - 10
of 4 541
pro vyhledávání: '"Hanghang An"'
Autor:
Yan, Yuchen, Chen, Yuzhong, Chen, Huiyuan, Li, Xiaoting, Xu, Zhe, Zeng, Zhichen, Liu, Zhining, Tong, Hanghang
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more intricate structures, encompassing (1) static heterophilic graphs enc
Externí odkaz:
http://arxiv.org/abs/2412.16435
Hybrid battery thermal management systems (HBTMS) combining active liquid cooling and passive phase change materials (PCM) cooling have shown a potential for the thermal management of lithium-ion batteries. However, the fill volume of coolant and PCM
Externí odkaz:
http://arxiv.org/abs/2412.00999
Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new in
Externí odkaz:
http://arxiv.org/abs/2412.10390
Autor:
Liu, Xiaolong, Zeng, Zhichen, Liu, Xiaoyi, Yuan, Siyang, Song, Weinan, Hang, Mengyue, Liu, Yiqun, Yang, Chaofei, Kim, Donghyun, Chen, Wen-Yen, Yang, Jiyan, Han, Yiping, Jin, Rong, Long, Bo, Tong, Hanghang, Yu, Philip S.
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into large-scale recommendation models. However, simply increasing the mode
Externí odkaz:
http://arxiv.org/abs/2411.13700
Autor:
Chen, Deming, Youssef, Alaa, Pendse, Ruchi, Schleife, André, Clark, Bryan K., Hamann, Hendrik, He, Jingrui, Laino, Teodoro, Varshney, Lav, Wang, Yuxiong, Sil, Avirup, Jabbarvand, Reyhaneh, Xu, Tianyin, Kindratenko, Volodymyr, Costa, Carlos, Adve, Sarita, Mendis, Charith, Zhang, Minjia, Núñez-Corrales, Santiago, Ganti, Raghu, Srivatsa, Mudhakar, Kim, Nam Sung, Torrellas, Josep, Huang, Jian, Seelam, Seetharami, Nahrstedt, Klara, Abdelzaher, Tarek, Eilam, Tamar, Zhao, Huimin, Manica, Matteo, Iyer, Ravishankar, Hirzel, Martin, Adve, Vikram, Marinov, Darko, Franke, Hubertus, Tong, Hanghang, Ainsworth, Elizabeth, Zhao, Han, Vasisht, Deepak, Do, Minh, Oliveira, Fabio, Pacifici, Giovanni, Puri, Ruchir, Nagpurkar, Priya
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co
Externí odkaz:
http://arxiv.org/abs/2411.13239
Autor:
Zeng, Zhichen, Liu, Xiaolong, Hang, Mengyue, Liu, Xiaoyi, Zhou, Qinghai, Yang, Chaofei, Liu, Yiqun, Ruan, Yichen, Chen, Laming, Chen, Yuxin, Hao, Yujia, Xu, Jiaqi, Nie, Jade, Liu, Xi, Zhang, Buyun, Wen, Wei, Yuan, Siyang, Wang, Kai, Chen, Wen-Yen, Han, Yiping, Li, Huayu, Yang, Chunzhi, Long, Bo, Yu, Philip S., Tong, Hanghang, Yang, Jiyan
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interest
Externí odkaz:
http://arxiv.org/abs/2411.09852
Due to the difficulty of acquiring large-scale explicit user feedback, implicit feedback (e.g., clicks or other interactions) is widely applied as an alternative source of data, where user-item interactions can be modeled as a bipartite graph. Due to
Externí odkaz:
http://arxiv.org/abs/2411.09181
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, people train a single general-purpose LLM and provide the LLM with differen
Externí odkaz:
http://arxiv.org/abs/2411.01992
Autor:
Ban, Yikun, Zou, Jiaru, Li, Zihao, Qi, Yunzhe, Fu, Dongqi, Kang, Jian, Tong, Hanghang, He, Jingrui
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been directed at solving this problem, including approaches based on similarity
Externí odkaz:
http://arxiv.org/abs/2411.01410
Graphs have been widely used in the past decades of big data and AI to model comprehensive relational data. When analyzing a graph's statistical properties, graph laws serve as essential tools for parameterizing its structure. Identifying meaningful
Externí odkaz:
http://arxiv.org/abs/2410.12126