Search Results for author: Tianshi Chen

Found 18 papers, 2 papers with code

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.

Evolutionary Algorithms

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?

Evolutionary Algorithms

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

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

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).

Benchmarking

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 regression +2

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.

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.

Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms

no code implementations6 Dec 2021 Biqiang Mu, Tianshi Chen, Changming Cheng, Er-Wei Bai

The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems.

Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator

no code implementations25 Sep 2022 Yue Ju, Biqiang Mu, Lennart Ljung, Tianshi Chen

Regularized system identification is the major advance in system identification in the last decade.

On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

no code implementations27 Sep 2022 Biqiang Mu, Tianshi Chen, He Kong, Bo Jiang, Lei Wang, Junfeng Wu

For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping.

Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems

no code implementations7 Mar 2023 Xian Yu, Xiaozhu Fang, Biqiang Mu, Tianshi Chen

For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen model structures.

Pushing the Limits of Machine Design: Automated CPU Design with AI

1 code implementation21 Jun 2023 Shuyao Cheng, Pengwei Jin, Qi Guo, Zidong Du, Rui Zhang, Yunhao Tian, Xing Hu, Yongwei Zhao, Yifan Hao, Xiangtao Guan, Husheng Han, Zhengyue Zhao, Ximing Liu, Ling Li, Xishan Zhang, Yuejie Chu, Weilong Mao, Tianshi Chen, Yunji Chen

By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours.

On Kernel Design for Regularized Non-Causal System Identification

no code implementations26 Jul 2023 Xiaozhu Fang, Tianshi Chen

Through one decade's development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging new system identification paradigm.

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