1 code implementation • 4 Jan 2024 • Jing Wu, Suiyao Chen, Qi Zhao, Renat Sergazinov, Chen Li, ShengJie Liu, Chongchao Zhao, Tianpei Xie, Hanqing Guo, Cheng Ji, Daniel Cociorva, Hakan Brunzel
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.
no code implementations • 15 Nov 2017 • Tianpei Xie, Sijia Liu, Alfred O. Hero III
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse.
no code implementations • 21 Oct 2016 • Tianpei Xie, Nasser. M. Narabadi, Alfred O. Hero
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.
no code implementations • 16 Jul 2015 • Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.
no code implementations • 5 Jul 2015 • Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero III
In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples.