Zobrazeno 1 - 10
of 7 699
pro vyhledávání: '"Ji Cheng"'
Autor:
Chen, Suiyao, Wu, Jing, Wang, Yunxiao, Ji, Cheng, Xie, Tianpei, Cociorva, Daniel, Sharps, Michael, Levasseur, Cecile, Brunzell, Hakan
Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive learning has led to significant advancements in fields like computer vis
Externí odkaz:
http://arxiv.org/abs/2411.11148
Autor:
Liang, Haotong, Wang, Chuangye, Yu, Heshan, Kirsch, Dylan, Pant, Rohit, McDannald, Austin, Kusne, A. Gilad, Zhao, Ji-Cheng, Takeuchi, Ichiro
Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experiment-theory cycles in practice are usually ad hoc, often inherently dif
Externí odkaz:
http://arxiv.org/abs/2410.17430
In this work, we introduce MOLA: a Multi-block Orthogonal Long short-term memory Autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by in
Externí odkaz:
http://arxiv.org/abs/2410.07508
Autor:
Sun, Qingyun, Chen, Ziying, Yang, Beining, Ji, Cheng, Fu, Xingcheng, Zhou, Sheng, Peng, Hao, Li, Jianxin, Yu, Philip S.
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retain
Externí odkaz:
http://arxiv.org/abs/2407.00615
Autor:
Ji, Cheng, Pettit, Robert M., Gupta, Shobhit, Grant, Gregory D., Masiulionis, Ignas, Sundaresh, Ananthesh, Deckoff--Jones, Skylar, Olberding, Max, Singh, Manish K., Heremans, F. Joseph, Guha, Supratik, Dibos, Alan M., Sullivan, Sean E.
Publikováno v:
Appl. Phys. Lett. 125, 084001 (2024)
Defects and dopant atoms in solid state materials are a promising platform for realizing single photon sources and quantum memories, which are the basic building blocks of quantum repeaters needed for long distance quantum networks. In particular, tr
Externí odkaz:
http://arxiv.org/abs/2406.02810
Autor:
Li, Qian, Ji, Cheng, Guo, Shu, Zhao, Yong, Mao, Qianren, Wang, Shangguang, Wei, Yuntao, Li, Jianxin
Multi-modal relation extraction (MMRE) is a challenging task that aims to identify relations between entities in text leveraging image information. Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing ver
Externí odkaz:
http://arxiv.org/abs/2404.12006
Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing
Externí odkaz:
http://arxiv.org/abs/2403.04521
Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networks (DGNNs) have shown impressive predictive abilities by exploiting th
Externí odkaz:
http://arxiv.org/abs/2402.06716
Autor:
Wu, Jing, Chen, Suiyao, Zhao, Qi, Sergazinov, Renat, Li, Chen, Liu, Shengjie, Zhao, Chongchao, Xie, Tianpei, Guo, Hanqing, Ji, Cheng, Cociorva, Daniel, Brunzel, Hakan
Publikováno v:
Association for the Advancement of Artificial Intelligence (AAAI), 2024
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies. However, applying these methods to tabular dat
Externí odkaz:
http://arxiv.org/abs/2401.02013
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generati
Externí odkaz:
http://arxiv.org/abs/2311.11114