Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Gong, Pinghua"'
Autor:
Cai, Qingpeng, Zhan, Ruohan, Zhang, Chi, Zheng, Jie, Ding, Guangwei, Gong, Pinghua, Zheng, Dong, Jiang, Peng
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and multi-faceted responses towards recommendations, including watch time
Externí odkaz:
http://arxiv.org/abs/2205.13248
Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/edges, static node/edge attributes, and spatiotemporal node/edge featur
Externí odkaz:
http://arxiv.org/abs/2010.04554
Publikováno v:
Pattern Recognition, 2023
Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelli
Externí odkaz:
http://arxiv.org/abs/2009.02653
Publikováno v:
In Pattern Recognition July 2023 139
Autor:
Yao, Huaxiu, Wu, Fei, Ke, Jintao, Tang, Xianfeng, Jia, Yitian, Lu, Siyu, Gong, Pinghua, Ye, Jieping, Li, Zhenhui
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets whi
Externí odkaz:
http://arxiv.org/abs/1802.08714
Autor:
Gong, Pinghua, Ye, Jieping
Stochastic gradient algorithms estimate the gradient based on only one or a few samples and enjoy low computational cost per iteration. They have been widely used in large-scale optimization problems. However, stochastic gradient algorithms are usual
Externí odkaz:
http://arxiv.org/abs/1406.1102
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However,
Externí odkaz:
http://arxiv.org/abs/1303.4434
In this article, we address the issue of recovering latent transparent layers from superimposition images. Here, we assume we have the estimated transformations and extracted gradients of latent layers. To rapidly recover high-quality image layers, w
Externí odkaz:
http://arxiv.org/abs/1211.4307
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the e
Externí odkaz:
http://arxiv.org/abs/1210.5806
Autor:
Afsari, Bahman, Airola, Antti, Aittokallio, Tero, Bivol, Adrian, Boehm, Jesse S., Bunte, Kerstin, Carlin, Daniel, Chang, Yu-Chuan, Chen, Tenghui, Chong, Zechen, Chopra, Sahil, Cowley, Glenn S., Deran, Alden, Ellrott, Kyle, Elmarakeby, Haitham, Fertig, Elana J., Gonçalves, Emanuel, Gönen, Mehmet, Gong, Pinghua, Gopalacharyulu, Peddinti, Graim, Kiley, Guan, Yuanfang, Hafemeister, Christoph, Hahn, William C., Heath, Lenwood, Hoff, Bruce, Howell, Sara, Jaiswal, Alok, Karasuyama, Masayuki, Kaski, Samuel, Kędziorski, Łukasz, Khan, Suleiman A., Khemka, Niraj, King, Erh-kan, Lauria, Mario, Liu, Mark, Machado, Daniel, Mamitsuka, Hiroshi, Marbach, Daniel, Margolin, Adam A., Mazurkiewicz, Mateusz, Menden, Michael P., Migacz, Szymon, Newton, Yulia, Ng, Sam, Nie, Zhi, Norman, Thea C., Pahikkala, Tapio, Paull, Evan, Praveen, Paurush, Priami, Corrado, Rizzetto, Simone, Rocha, Miguel, Root, David E., Rudd, Cameron, Rudnicki, Witold R., Saez-Rodriguez, Julio, Sokolov, Artem, Song, Lei, Stolovitzky, Gustavo, Stuart, Joshua M., Sun, Duanchen, Szalai, Bence, Tang, Hao, Tang, Jing, Tsherniak, Aviad, Uzunangelov, Vladislav, Vazquez, Francisca, Wang, Tao, Wang, Difei, Weir, Barbara A., Wennerberg, Krister, Wu, Ling-yun, Xiao, Guanghua, Xie, Yang, Ye, Jieping, Ye, Yuting, Zhan, Xiaowei, Zhou, Wanding, Zhu, Fan
Publikováno v:
In Cell Systems 22 November 2017 5(5):485-497