The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It au...The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.展开更多
The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear p...The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.展开更多
Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observ...Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observations.This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes(cGP),Deep Gaussian processes(DGP),encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting(XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V.We specifically assess their capabilities in predicting correlated material properties,including yield strength,hardness,modulus,ultimate tensile strength,elongation,and average hardness under dynamic/quasi-static conditions,alongside auxiliary computational properties.The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic,heterotopic,and incomplete data commonly encountered in materials science.Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperformother surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge.This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and dataefficient materials design.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No 60972106the China Postdoctoral Science Foundation under Grant No 2014M561053+1 种基金the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108the Hebei Province Natural Science Foundation under Grant No E2016202341
文摘The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.
基金supported by the Natural Science Foundation of Jiangsu Province(BK2012510,BK20140074)the National Postdoctoral Foundation of China(20090461424)
文摘The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.
基金supported by the Texas A&M University System National Laboratories Office of the Texas A&M University System and Los Alamos National Laboratory as part of the Joint Research Collaboration Program. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Los Alamos National Laboratory or The Texas A&M University Systemsupport from the U.S. Department of Energy (DOE) ARPA-E CHADWICK Program through Project DE‐AR0001988JJ acknowledges support from the Los Alamos National Laboratory Laboratory (LANL) Laboratory Directed Research and Development Program under project number 20220815PRD4. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218CNA000001). Original data were generated within the BIRDSHOT Center (https://birdshot.tamu.edu), supported by the Army Research Laboratory under Cooperative Agreement (CA) NumberW911NF-22-2-0106 (MM, DK, DA, VA and RA acknowledge partial support from this CA). NF acknowledges support from AFRL through a subcontract with ARCTOS, TOPS VI (165852-19F5830-19-02-C1). Calculations were carried out at Texas A&M High-Performance Research Computing (HPRC).
文摘Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observations.This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes(cGP),Deep Gaussian processes(DGP),encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting(XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V.We specifically assess their capabilities in predicting correlated material properties,including yield strength,hardness,modulus,ultimate tensile strength,elongation,and average hardness under dynamic/quasi-static conditions,alongside auxiliary computational properties.The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic,heterotopic,and incomplete data commonly encountered in materials science.Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperformother surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge.This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and dataefficient materials design.
