Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Biye Jiang"'
Autor:
Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang, Biye Jiang, Shuguang Han, Hongbo Deng, Bo Zheng
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9edb5336fca0bdbc199b1eabe6ae2d4
https://doi.org/10.1145/3511808.3557479
https://doi.org/10.1145/3511808.3557479
Autor:
Zhe Wang, Guorui Zhou, Ruiming Tang, Xiaoqiang Zhu, Biye Jiang, Kan Ren, Weinan Zhang, Qingyao Ai, Kuang-Chih Lee
Publikováno v:
KDD
Recently, we have witnessed that deep learning-based approaches has been widely applied to empower many internet-scale applications. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it diff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5e321ea67d9afb1b6a7415be57147c8
https://doi.org/10.1145/3447548.3469444
https://doi.org/10.1145/3447548.3469444
Autor:
Yu Zhang, Xinyang Guo, Guorui Zhou, Huimin Yi, Biye Jiang, Di Zhang, Peng Sun, Kun Gai, Yang Zheng, Jian Xu, Xiaoqiang Zhu, Zhi Wang, Dongyue Wang, Liqin Zhao, Zelin Hu, Yue Song, Jinhui Li, Chao Deng, Sui Huang
Publikováno v:
Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data.
With the rapid growth of data and computing power, deep learning based approaches have become the main solution for many artificial intelligence problems such as image classification, speech recognition and computer vision. Several excellent deep lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9839bb3d023e5e8f80b92f9b27aefb9f
https://doi.org/10.1145/3326937.3341255
https://doi.org/10.1145/3326937.3341255
Autor:
Biye Jiang, John Canny
Publikováno v:
IUI
Machine learning is growing in importance in industry, sciences, and many other fields. In many and perhaps most of these applications, users need to trade off competing goals. Machine learning, however, has evolved around the optimization of a singl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2769fcf083c592441aa209ecb490abe4
https://doi.org/10.1145/3025171.3025172
https://doi.org/10.1145/3025171.3025172
Autor:
Pierre Karashchu, Biye Jiang, John Canny, James A. Landay, Rocio Francesca Guerrero, Vasilis Oikonomou, Pablo Paredes, Coye Cheshire, Terrie Yang
Publikováno v:
CSCW Companion
This demo presents an instance of Inquire, a tool designed to support qualitative researchers in the early stages of research. The tool enables the search over millions of users' records to extract early insights to aid in the formulation of research
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdd7ca8fd7ccf4b17056b1f985bac8ef
https://doi.org/10.1145/3022198.3023272
https://doi.org/10.1145/3022198.3023272
Publikováno v:
ACM Transactions on Graphics. 32:1-11
We study the problem of inverse image editing , which recovers a semantically-meaningful editing history from a source image and an edited copy. Our approach supports a wide range of commonly-used editing operations such as cropping, object insertion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0834998bf8f0c58f6bd6c5ef4e33a41c
https://doi.org/10.1145/2508363.2508371
https://doi.org/10.1145/2508363.2508371
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics. 19:1808-1819
Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75bf0607c82db0ec54fe4f8ab714739a
https://doi.org/10.1109/tvcg.2013.99
https://doi.org/10.1109/tvcg.2013.99
Publikováno v:
Computer Graphics Forum. 32:421-430
Data analysts must make sense of increasingly large data sets, sometimes with billions or more records. We present methods for interactive visualization of big data, following the principle that perceptual and interactive scalability should be limite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fb108ff04c6b999bdbc753d59726b6d
https://doi.org/10.1111/cgf.12129
https://doi.org/10.1111/cgf.12129
Publikováno v:
KDD
Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. SAME (State Augmentation for Marginal Estimation) [15, 8] is an approac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e2b30b78b7938747155154e27bb7f21
https://doi.org/10.1145/2783258.2783416
https://doi.org/10.1145/2783258.2783416