Zobrazeno 1 - 10
of 158
pro vyhledávání: '"Wu, Xiangping"'
Autor:
Jiang, Shuoran, Chen, Qingcai, Pan, Youchen, Xiang, Yang, Lin, Yukang, Wu, Xiangping, Liu, Chuanyi, Song, Xiaobao
Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes in a zeroth-order SGD optimizer (ZO-SGD), demonstrating excellent perfo
Externí odkaz:
http://arxiv.org/abs/2312.15184
The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfit to the table style in training set, and suffer from significant perf
Externí odkaz:
http://arxiv.org/abs/2312.11043
The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA)
Externí odkaz:
http://arxiv.org/abs/2310.16062
Autor:
GRAND Collaboration, Batista, Rafael Alves, Benoit-Lévy, Aurélien, Bister, Teresa, Bustamante, Mauricio, Chen, Yiren, Cheng, LingMei, Chiche, Simon, Colley, Jean-Marc, Correa, Pablo, Laurenciu, Nicoleta Cucu, Dai, Zigao, de Errico, Beatriz, de Jong, Sijbrand, Neto, João R. T. de Mello, de Vries, Krijn D., Denton, Peter B., Deocoene, Valentin, Duan, Kaikai, Duan, Bohao, Engel, Ralph, Fan, Yizhong, Ferrière, Arsène, Gou, QuanBu, Gu, Junhua, Guelfand, Marion, Guo, Jianhua, Guo, Yiqing, Gupta, Vaidhai, Guépin, Claire, Gülzow, Lukas, Haungs, Andreas, He, Haoning, Hivon, Eric, Hu, Hongbo, Huang, Xiaoyuan, Huang, Yan, Huege, Tim, Jiang, Wen, Koirala, Ramesh, Kotera, Kumiko, Köhler, Jelena, Lago, Bruno L., Coz, Sandra Le, Legrand, François, Leisos, Antonios, Li, Rui, Liu, Cheng, Liu, Ruoyu, Liu, Wei, Ma, Pengxiong, Macias, Oscar, Magnard, Frédéric, Martineau-Huynh, Olivier, Mikhno, Ananstasiia, Mitra, Pragati, Mostafá, Miguel, Mottez, Fabrice, Mouette, Jean, Murase, Kohta, Niess, Valentin, Nonis, Stavros, Ogio, Shoichi, Oikonomou, Foteini, Pierog, Tanguy, Piotrowski, Lech Wiktor, Poisvert, Pierre, Prunet, Simon, Qian, Xiangli, Roth, Markus, Sako, Takashi, Schoorlemmer, Harm, Steeman, Bart, Szálas-Motesiczky, Dániel, Sławiński, Szymon, Timmermans, Anne, Timmermans, Charles, Tsirigotis, Apostolos, Tueros, Matías, Wang, Shen, Wang, Xiangyu, Wang, Xu, Wei, Daming, Wei, Feng, Wu, Xiangping, Wu, Xuefeng, Xu, Xin, Xu, Xing, Yang, Lili, Yang, Xuan, Yuan, Qiang, Zarka, Philippe, Zeng, Houdun, Zhang, Chao, Zhang, Jianli, Zhang, Kewen, Zhang, Pengfei, Zhang, Songbo, Zhou, Hao
The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the
Externí odkaz:
http://arxiv.org/abs/2308.00120
The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has discovered more than 650 new pulsars, which account for 20% of our known Galactic pulsar population. In this paper, we estimate the prospect of a pulsar survey with a radio telescop
Externí odkaz:
http://arxiv.org/abs/2307.03422
Autor:
Xu, Heng, Chen, Siyuan, Guo, Yanjun, Jiang, Jinchen, Wang, Bojun, Xu, Jiangwei, Xue, Zihan, Caballero, R. Nicolas, Yuan, Jianping, Xu, Yonghua, Wang, Jingbo, Hao, Longfei, Luo, Jingtao, Lee, Kejia, Han, Jinlin, Jiang, Peng, Shen, Zhiqiang, Wang, Min, Wang, Na, Xu, Renxin, Wu, Xiangping, Manchester, Richard, Qian, Lei, Guan, Xin, Huang, Menglin, Sun, Chun, Zhu, Yan
Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of wid
Externí odkaz:
http://arxiv.org/abs/2306.16216
Publikováno v:
In Mechanism and Machine Theory August 2024 198
Publikováno v:
In Neural Networks May 2024 173
Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did not explore the power of the unsupervised word emb
Externí odkaz:
http://arxiv.org/abs/2004.13844
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