no code implementations • 26 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.
1 code implementation • 21 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.
no code implementations • 7 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.
no code implementations • 27 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.
no code implementations • 26 Sep 2022 • Junpeng Zhang, Yue Ju, Biqiang Mu, Renxin Zhong, Tianshi Chen
Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling.
no code implementations • 25 Sep 2022 • Yue Ju, Biqiang Mu, Lennart Ljung, Tianshi Chen
Regularized system identification is the major advance in system identification in the last decade.
no code implementations • 19 Aug 2022 • Husheng Han, Xing Hu, Kaidi Xu, Pucheng Dang, Ying Wang, Yongwei Zhao, Zidong Du, Qi Guo, Yanzhi Yang, Tianshi Chen
This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection.
no code implementations • 6 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.
1 code implementation • NeurIPS 2021 • Zhixing Du, Rui Zhang, Ming Chang, Xishan Zhang, Shaoli Liu, Tianshi Chen, Yunji Chen
Second, these methods imitate some features which are mistakenly regarded as the background by the teacher detector.
no code implementations • 8 Jul 2020 • Yiping Jiang, Tianshi Chen
In high-dimensional settings, sparse structures are critical for efficiency in term of memory and computation complexity.
no code implementations • 3 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.
no code implementations • 21 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.
no code implementations • 23 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).
no code implementations • 2 Jul 2015 • Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung
In this paper, a comparative study of estimators based on these different types of regularizers is reported.
no code implementations • 20 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.
no code implementations • 20 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.
no code implementations • 28 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?
no code implementations • 23 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.