Search Results for author: Tianshi Chen

Found 9 papers, 0 papers with code

Accelerated Sparse Bayesian Learning via Screening Test and Its Applications

no code implementations8 Jul 2020 Yiping Jiang, Tianshi Chen

In high-dimensional settings, sparse structures are critical for efficiency in term of memory and computation complexity.

DWM: A Decomposable Winograd Method for Convolution Acceleration

no code implementations3 Feb 2020 Di Huang, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, Qi Guo, Zidong Du, Shaoli Liu, Tianshi Chen, Yunji Chen

In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.

Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

no code implementations21 Apr 2019 Feng Yin, Lishuo Pan, Xinwei He, Tianshi Chen, Sergios Theodoridis, Zhi-Quan, Luo

Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications.

Gaussian Processes Time Series

BENCHIP: Benchmarking Intelligence Processors

no code implementations23 Oct 2017 Jinhua Tao, Zidong Du, Qi Guo, Huiying Lan, Lei Zhang, Shengyuan Zhou, Lingjie Xu, Cong Liu, Haifeng Liu, Shan Tang, Allen Rush, Willian Chen, Shaoli Liu, Yunji Chen, Tianshi Chen

The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware).

Maximum Entropy Kernels for System Identification

no code implementations20 Nov 2014 Francesca Paola Carli, Tianshi Chen, Lennart Ljung

In this paper we show that maximum entropy properties indeed extend to the whole family of DC kernels.

Matrix Completion

Scalable Anomaly Detection in Large Homogenous Populations

no code implementations20 Sep 2013 Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung, S. Shankar Sastry

The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problems of practical interests.

Anomaly Detection Combinatorial Optimization

On the Easiest and Hardest Fitness Functions

no code implementations28 Mar 2012 Jun He, Tianshi Chen, Xin Yao

The aim of this paper is to answer the following research questions: Given a fitness function class, which functions are the easiest with respect to an evolutionary algorithm?

Novel Analysis of Population Scalability in Evolutionary Algorithms

no code implementations23 Aug 2011 Jun He, Tianshi Chen, Boris Mitavskiy

(1) We demonstrate rigorously that for elitist EAs with identical global mutation, using a lager population size always increases the average rate of convergence to the optimal set; and yet, sometimes, the expected number of generations needed to find an optimal solution (measured by either the maximal value or the average value) may increase, rather than decrease.

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