Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Yan, Jingkai"'
Autor:
Yan, Jingkai
Deep learning has seen rapid evolution in the past decade, accomplishing tasks that were previously unimaginable. At the same time, researchers strive to better understand and interpret the underlying mechanisms of the deep models, which are often ju
Autor:
Yan, Jingkai, Wang, Shiyu, Wei, Xinyu Rain, Wang, Jimmy, Márka, Zsuzsanna, Márka, Szabolcs, Wright, John
In scientific and engineering scenarios, a recurring task is the detection of low-dimensional families of signals or patterns. A classic family of approaches, exemplified by template matching, aims to cover the search space with a dense template bank
Externí odkaz:
http://arxiv.org/abs/2310.10039
Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with
Externí odkaz:
http://arxiv.org/abs/2207.11583
Autor:
Colgan, Robert E., Márka, Zsuzsa, Yan, Jingkai, Bartos, Imre, Wright, John N., Márka, Szabolcs
As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among t
Externí odkaz:
http://arxiv.org/abs/2203.05086
Achieving invariance to nuisance transformations is a fundamental challenge in the construction of robust and reliable vision systems. Existing approaches to invariance scale exponentially with the dimension of the family of transformations, making t
Externí odkaz:
http://arxiv.org/abs/2203.05006
Autor:
Colgan, Robert E., Yan, Jingkai, Márka, Zsuzsa, Bartos, Imre, Márka, Szabolcs, Wright, John N.
As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of o
Externí odkaz:
http://arxiv.org/abs/2202.13486
Autor:
Gibson, Elizabeth A., Zhang, Junhui, Yan, Jingkai, Chillrud, Lawrence, Benavides, Jaime, Nunez, Yanelli, Herbstman, Julie B., Goldsmith, Jeff, Wright, John, Kioumourtzoglou, Marianthi-Anna
Environmental health researchers often aim to identify sources/behaviors that give rise to potentially harmful exposures. We adapted principal component pursuit (PCP)-a robust technique for dimensionality reduction in computer vision and signal proce
Externí odkaz:
http://arxiv.org/abs/2111.00104
Autor:
Zhang, Haifeng, Yang, Xuzhuang, Gao, Guanjun, Liu, Ying, Zhao, Min, Yan, Jingkai, Du, Haoran, Xiao, Xiran, Su, Haiquan
Publikováno v:
In Fuel 15 June 2024 366
We propose a new framework -- Square Root Principal Component Pursuit -- for low-rank matrix recovery from observations corrupted with noise and outliers. Inspired by the square root Lasso, this new formulation does not require prior knowledge of the
Externí odkaz:
http://arxiv.org/abs/2106.09211
Autor:
Yan, Jingkai, Avagyan, Mariam, Colgan, Robert E., Veske, Doğa, Bartos, Imre, Wright, John, Márka, Zsuzsa, Márka, Szabolcs
Gravitational wave science is a pioneering field with rapidly evolving data analysis methodology currently assimilating and inventing deep learning techniques. The bulk of the sophisticated flagship searches of the field rely on the time-tested match
Externí odkaz:
http://arxiv.org/abs/2104.03961