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- Wei Dai, Olgica Milenkovic
- IEEE Transactions on Information Theory
- 2009

We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same… (More)

- Wei Dai, Youjian Liu, Brian Rider
- IEEE Transactions on Information Theory
- 2008

The Grassmann manifold G<sub>n,p</sub> (L) is the set of all p-dimensional planes (through the origin) in the n-dimensional Euclidean space L<sup>n</sup>, where L is either R or C. This paper considers the quantization problem in which a source in G<sub>n,p</sub> (L) is quantized through a code in G<sub>n,q</sub> (L), with p and q not necessarily the same.… (More)

- Jinhui Yuan, Fei Gao, +6 authors Wei-Ying Ma
- ArXiv
- 2014

When building large-scale machine learning (ML) programs, such as massive topics models or deep networks with up to trillions of parameters and training examples, one usually assumes that such massive tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers. We… (More)

- Wei Dai, Olgica Milenkovic
- ArXiv
- 2008

— We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques, and reconstruction accuracy of the same order as that of LP optimization… (More)

- Eric P. Xing, Qirong Ho, +7 authors Yaoliang Yu
- IEEE Transactions on Big Data
- 2015

How can one build a distributed framework that allows efficient deployment of a wide spectrum of modern advanced machine learning (ML) programs for industrial-scale problems using Big Models (100s of billions of parameters) on Big Data (terabytes or petabytes)- Contemporary parallelization strategies employ fine-grained operations and scheduling beyond the… (More)

- Wei Dai, Hoa Vinh Pham, Olgica Milenkovic
- 2009 IEEE International Symposium on Information…
- 2009

We study the average distortion introduced by scalar, vector, and entropy coded quantization of compressive sensing (CS) measurements. The asymptotic behavior of the underlying quantization schemes is either quantified exactly or characterized via bounds. We also modify two benchmark CS reconstruction algorithms to accommodate quantization effects, and… (More)

- Jinhui Yuan, Fei Gao, +6 authors Wei-Ying Ma
- WWW
- 2015

When building large-scale machine learning (ML) programs, such as massive topic models or deep neural networks with up to trillions of parameters and training examples, one usually assumes that such massive tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners and academic… (More)

- Wei Dai, Qi Wang, +7 authors Chinthalapally V Rao
- Cancer research
- 2004

A compromised spindle checkpoint is thought to play a key role in genetic instability that predisposes cells to malignant transformation. Loss of function mutations of BubR1, an important component of the spindle checkpoint, have been detected in human cancers. Here we show that BubR1(+/-) mouse embryonic fibroblasts are defective in spindle checkpoint… (More)

- Wei Dai, Xiaowei Chen
- Mathematical and Computer Modelling
- 2012

- Wei Dai, Tao Xu, Wenwu Wang
- IEEE Transactions on Signal Processing
- 2012

We consider the data-driven dictionary learning problem. The goal is to seek an over-complete dictionary from which every training signal can be best approximated by a linear combination of only a few codewords. This task is often achieved by iteratively executing two operations: sparse coding and dictionary update. The focus of this paper is on the… (More)