Stem gaussian$ (all stems)

225 papers:

CASE-2015-SterlingSZC #algorithm #optimisation #parametricity #process

Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm (DS, TS, YZ, HC), pp. 1490–1496.

DRR-2015-FengPL #process #recognition

Gaussian process style transfer mapping for historical Chinese character recognition (JF, LP, FL).

STOC-2015-AggarwalDRS #problem #using

Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract (DA, DD, OR, NSD), pp. 733–742.

STOC-2015-CousinsV #algorithm

Bypassing KLS: Gaussian Cooling and an O^*(n3) Volume Algorithm (BC, SV), pp. 539–548.

STOC-2015-GeHK #learning

Learning Mixtures of Gaussians in High Dimensions (RG, QH, SMK), pp. 761–770.

STOC-2015-HardtP #bound #learning

Tight Bounds for Learning a Mixture of Two Gaussians (MH, EP), pp. 753–760.

ICML-2015-DeisenrothN #distributed #process

Distributed Gaussian Processes (MPD, JWN), pp. 1481–1490.

ICML-2015-FilipponeE #linear #probability #process #scalability

Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) (MF, RE), pp. 1015–1024.

ICML-2015-FlaxmanWNNS #performance #process

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods (SF, AGW, DN, HN, AJS), pp. 607–616.

ICML-2015-GalCG #category theory #estimation #multi #process

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data (YG, YC, ZG), pp. 645–654.

ICML-2015-GalT #approximate #nondeterminism #process #representation

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs (YG, RT), pp. 655–664.

ICML-2015-GarnettHS #process

Finding Galaxies in the Shadows of Quasars with Gaussian Processes (RG, SH, JS), pp. 1025–1033.

ICML-2015-HoangHL #big data #framework #modelling #probability #process

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data (TNH, QMH, BKHL), pp. 569–578.

ICML-2015-Kandemir #learning #process #symmetry

Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.

ICML-2015-LiangP #process

Landmarking Manifolds with Gaussian Processes (DL, JP), pp. 466–474.

ICML-2015-LloydGOR #process

Variational Inference for Gaussian Process Modulated Poisson Processes (CML, TG, MAO, SJR), pp. 1814–1822.

ICML-2015-MacdonaldHH #modelling #process

Controversy in mechanistic modelling with Gaussian processes (BM, CFH, DH), pp. 1539–1547.

ICML-2015-SamoR #parametricity #process #scalability

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes (YLKS, SR), pp. 2227–2236.

ICML-2015-SuiGBK #optimisation #process

Safe Exploration for Optimization with Gaussian Processes (YS, AG, JWB, AK), pp. 997–1005.

ICML-2015-WilsonN #kernel #process #scalability

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) (AGW, HN), pp. 1775–1784.

ICML-2015-YuanHTLC #modelling

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood (XY, RH, ET, RL, LC), pp. 1254–1263.

KDD-2015-YuanBM #process #re-engineering #using

Gas Concentration Reconstruction for Coal-Fired Boilers Using Gaussian Process (CY, MB, BM), pp. 2247–2256.

RecSys-2015-Steck #matrix #ranking

Gaussian Ranking by Matrix Factorization (HS), pp. 115–122.

CASE-2014-MahlerKLSMKPWFAG #learning #process #using

Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (JM, SK, ML, SS, AM, BK, SP, JW, MF, PA, KYG), pp. 532–539.

DATE-2014-LangeSJHLS #correlation #modelling #parametricity #probability #standard

Probabilistic standard cell modeling considering non-Gaussian parameters and correlations (AL, CS, RJ, JH, IL, US), pp. 1–4.

ICML-c1-2014-NguyenB #performance #process

Fast Allocation of Gaussian Process Experts (TVN, EVB), pp. 145–153.

ICML-c2-2014-BarberW #difference #equation #estimation #process

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations (DB, YW), pp. 1485–1493.

ICML-c2-2014-BratieresQNG #graph #grid #predict #process #scalability

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications (SB, NQ, SN, ZG), pp. 334–342.

ICML-c2-2014-ContalPV #optimisation #process

Gaussian Process Optimization with Mutual Information (EC, VP, NV), pp. 253–261.

ICML-c2-2014-GrandeWH #learning #performance #process

Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.

ICML-c2-2014-HoangLJK #learning #process

Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes (TNH, BKHL, PJ, MSK), pp. 739–747.

ICML-c2-2014-LiuSD #approximate #modelling #visual notation

Gaussian Approximation of Collective Graphical Models (LPL, DS, TGD), pp. 1602–1610.

ICML-c2-2014-MengEH #learning #modelling #visual notation

Learning Latent Variable Gaussian Graphical Models (ZM, BE, AOHI), pp. 1269–1277.

ICML-c2-2014-ReyRF

Sparse meta-Gaussian information bottleneck (MR, VR, TJF), pp. 910–918.

ICML-c2-2014-RodriguesPR #classification #learning #multi #process

Gaussian Process Classification and Active Learning with Multiple Annotators (FR, FCP, BR), pp. 433–441.

ICML-c2-2014-ToshD #bound

Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians (CT, SD), pp. 1467–1475.

ICPR-2014-ChamroukhiBG #clustering #parametricity

Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering (FC, MB, HG), pp. 1460–1465.

ICPR-2014-Filippone #classification #process #pseudo

Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC (MF), pp. 614–619.

ICPR-2014-GuerreroR #process

Circular Regression Based on Gaussian Processes (PG, JRdS), pp. 3672–3677.

ICPR-2014-JainCL #image

Local Binary Patterns Calculated over Gaussian Derivative Images (VJ, JLC, AL), pp. 3987–3992.

ICPR-2014-YamashitaTYYF #strict

To Be Bernoulli or to Be Gaussian, for a Restricted Boltzmann Machine (TY, MT, EY, YY, HF), pp. 1520–1525.

KDIR-2014-BigdeliMRM #clustering #summary

Arbitrary Shape Cluster Summarization with Gaussian Mixture Model (EB, MM, BR, SM), pp. 43–52.

RecSys-2014-VanchinathanNBK #process #recommendation

Explore-exploit in top-N recommender systems via Gaussian processes (HPV, IN, FDB, AK), pp. 225–232.

SIGIR-2014-NguyenKB #process #recommendation

Gaussian process factorization machines for context-aware recommendations (TVN, AK, LB), pp. 63–72.

DAC-2013-KuruvillaSPVC #analysis #optimisation #set #statistics

Speeding up computation of the max/min of a set of gaussians for statistical timing analysis and optimization (VK, DS, JP, CV, NC), p. 7.

ICML-c1-2013-GilboaSCG #approximate #multi #process #scalability #using

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations (EG, YS, JPC, EG), pp. 454–461.

ICML-c1-2013-HanL13a #analysis #component

Principal Component Analysis on non-Gaussian Dependent Data (FH, HL), pp. 240–248.

ICML-c1-2013-WongAF #adaptation #modelling #visual notation

Adaptive Sparsity in Gaussian Graphical Models (EW, SPA, PTF), pp. 311–319.

ICML-c2-2013-Lopez-PazHG #dependence #multi #process

Gaussian Process Vine Copulas for Multivariate Dependence (DLP, JMHL, ZG), pp. 10–18.

ICML-c3-2013-KhanAFS #modelling #performance

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models (MEK, AYA, MPF, MWS), pp. 951–959.

ICML-c3-2013-RossD #constraints #parametricity #process

Nonparametric Mixture of Gaussian Processes with Constraints (JCR, JGD), pp. 1346–1354.

ICML-c3-2013-WilsonA #kernel #process

Gaussian Process Kernels for Pattern Discovery and Extrapolation (AGW, RPA), pp. 1067–1075.

ICML-c3-2013-WytockK #algorithm #energy #random #theory and practice

Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting (MW, JZK), pp. 1265–1273.

ICML-c3-2013-YangLZ #learning #matrix #multi

Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (MY, YL, ZZ), pp. 423–431.

KDD-2013-LiuYK #adaptation #modelling #process #using

Adaptive collective routing using gaussian process dynamic congestion models (SL, YY, RK), pp. 704–712.

KDD-2013-Vatsavai #approach #learning #multi #using

Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery (RRV), pp. 1419–1426.

CASE-2012-AnKP #learning #modelling #process

Grasp motion learning with Gaussian Process Dynamic Models (BA, HK, FCP), pp. 1114–1119.

ICML-2012-BoukouvalasBC #process #using

Gaussian Process Quantile Regression using Expectation Propagation (AB, RB, DC), p. 123.

ICML-2012-ChenCK #optimisation #process

Joint Optimization and Variable Selection of High-dimensional Gaussian Processes (BC, RMC, AK), p. 179.

ICML-2012-DesautelsKB #optimisation #process #trade-off

Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization (TD, AK, JWB), p. 109.

ICML-2012-FreitasSZ #bound #exponential #process

Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (NdF, AJS, MZ), p. 125.

ICML-2012-LiuHYLW #modelling #visual notation

High Dimensional Semiparametric Gaussian Copula Graphical Models (HL, FH, MY, JDL, LAW), p. 225.

ICML-2012-NaimG #algorithm #convergence

Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients (IN, DG), p. 185.

ICML-2012-ParrishG #reduction

Dimensionality Reduction by Local Discriminative Gaussians (NP, MRG), p. 30.

ICML-2012-WilsonKG #network #process

Gaussian Process Regression Networks (AGW, DAK, ZG), p. 149.

ICPR-2012-AhmedA #gesture #modelling #using

Flow Modeling and skin-based Gaussian pruning to recognize gestural actions using HMM (ORA, AAH), pp. 3488–3491.

ICPR-2012-BoulmerkaA #segmentation #using

Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions (AB, MSA), pp. 2894–2897.

ICPR-2012-DhallG #estimation #process

Group expression intensity estimation in videos via Gaussian Processes (AD, RG), pp. 3525–3528.

ICPR-2012-FreytagFRD #kernel #performance #process #segmentation #semantics

Efficient semantic segmentation with Gaussian processes and histogram intersection kernels (AF, BF, ER, JD), pp. 3313–3316.

ICPR-2012-HeLL #image #using

Single image super-resolution using Gaussian Mixture Model (HH, JL, XL), pp. 1916–1919.

ICPR-2012-Imajo #algorithm #performance #using

Fast Gaussian filtering algorithm using splines (KI), pp. 489–492.

ICPR-2012-KhanER #detection #image

A Gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images (AMK, HED, NMR), pp. 149–152.

ICPR-2012-LiuBFF

Complex Gaussian Mixture Model for fingerprint minutiae (CL, JB, XF, JF), pp. 545–548.

ICPR-2012-RiveraRC #recognition

Local Gaussian Directional Pattern for face recognition (ARR, JARC, OC), pp. 1000–1003.

ICPR-2012-SchwanderSNB

k-MLE for mixtures of generalized Gaussians (OS, AJS, FN, YB), pp. 2825–2828.

ICPR-2012-SuematsuH #process

Time series alignment with Gaussian processes (NS, AH), pp. 2355–2358.

ICPR-2012-TsuboshitaKFO #adaptation #image #using

Image annotation using adapted Gaussian mixture model (YT, NK, MF, MO), pp. 1346–1350.

KDIR-2012-DubeyBP #named

BINGR: Binary Search based Gaussian Regression (HD, SB, VP), pp. 258–263.

CAV-2012-HanJ #satisfiability

When Boolean Satisfiability Meets Gaussian Elimination in a Simplex Way (CSH, JHRJ), pp. 410–426.

DATE-2011-LiuHRG #optimisation #process #using

Global optimization of integrated transformers for high frequency microwave circuits using a Gaussian process based surrogate model (BL, YH, PR, GGEG), pp. 1101–1106.

ICDAR-2011-BukhariSB #set #using

Text-Line Extraction Using a Convolution of Isotropic Gaussian Filter with a Set of Line Filters (SSB, FS, TMB), pp. 579–583.

ICDAR-2011-NguyenB #2d #feature model #verification

An Application of the 2D Gaussian Filter for Enhancing Feature Extraction in Off-line Signature Verification (VN, MB), pp. 339–343.

DUXU-v2-2011-Hofman #modelling #statistics

Range Statistics and the Exact Modeling of Discrete Non-Gaussian Distributions on Learnability Data (RH), pp. 421–430.

ICML-2011-Lazaro-GredillaT #process

Variational Heteroscedastic Gaussian Process Regression (MLG, MKT), pp. 841–848.

ICML-2011-MarlinKM #bound #modelling

Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models (BMM, MEK, KPM), pp. 633–640.

KDD-2011-HuangLYFCWR #effectiveness #modelling #network

Brain effective connectivity modeling for alzheimer’s disease by sparse gaussian bayesian network (SH, JL, JY, AF, KC, TW, ER), pp. 931–939.

SAC-2011-HeinenE #incremental #modelling #using

Incremental feature-based mapping from sonar data using Gaussian mixture models (MRH, PME), pp. 1370–1375.

CASE-2010-Tobon-MejiaMZT #markov

A mixture of Gaussians Hidden Markov Model for failure diagnostic and prognostic (DATM, KM, NZ, GT), pp. 338–343.

DAC-2010-DhimanMR #modelling #online #predict #using

A system for online power prediction in virtualized environments using Gaussian mixture models (GD, KM, TR), pp. 807–812.

STOC-2010-KalaiMV #learning

Efficiently learning mixtures of two Gaussians (ATK, AM, GV), pp. 553–562.

ICML-2010-BardenetK #algorithm #optimisation

Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm (RB, BK), pp. 55–62.

ICML-2010-HonorioS #learning #modelling #multi #visual notation

Multi-Task Learning of Gaussian Graphical Models (JH, DS), pp. 447–454.

ICML-2010-KimT10a #learning #multi #process

Gaussian Processes Multiple Instance Learning (MK, FDlT), pp. 535–542.

ICML-2010-SaatciTR #modelling #process

Gaussian Process Change Point Models (YS, RDT, CER), pp. 927–934.

ICML-2010-Seeger #scalability

Gaussian Covariance and Scalable Variational Inference (MWS), pp. 967–974.

ICML-2010-SrinivasKKS #design #optimisation #process

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (NS, AK, SK, MWS), pp. 1015–1022.

ICML-2010-YanQ #process

Sparse Gaussian Process Regression via L1 Penalization (FY, Y(Q), pp. 1183–1190.

ICPR-2010-Allili #retrieval #using

Wavelet-Based Texture Retrieval Using a Mixture of Generalized Gaussian Distributions (MSA), pp. 3143–3146.

ICPR-2010-AriA #estimation #modelling #optimisation #using

Maximum Likelihood Estimation of Gaussian Mixture Models Using Particle Swarm Optimization (CA, SA), pp. 746–749.

ICPR-2010-AsheriRPR #adaptation #fault #framework #kernel #process

A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels (HA, HRR, NP, MHR), pp. 4541–4544.

ICPR-2010-CandamoGKG #detection #using

Detecting Wires in Cluttered Urban Scenes Using a Gaussian Model (JC, DBG, RK, SG), pp. 432–435.

ICPR-2010-ChengQHJT #estimation #process #recognition

Group Activity Recognition by Gaussian Processes Estimation (ZC, LQ, QH, SJ, QT), pp. 3228–3231.

ICPR-2010-JunG #classification #process

Nearest-Manifold Classification with Gaussian Processes (GJ, JG), pp. 914–917.

ICPR-2010-Martinez-UsoPS #image #segmentation

A Semi-supervised Gaussian Mixture Model for Image Segmentation (AMU, FP, JMS), pp. 2941–2944.

ICPR-2010-MemonLM #modelling #verification

Information Theoretic Expectation Maximization Based Gaussian Mixture Modeling for Speaker Verification (SM, ML, NCM), pp. 4536–4540.

ICPR-2010-NacereddineTZH10a #algorithm #image #modelling #segmentation #symmetry

Asymmetric Generalized Gaussian Mixture Models and EM Algorithm for Image Segmentation (NN, ST, DZ, LH), pp. 4557–4560.

ICPR-2010-SlimaneKAIH #modelling #recognition

Gaussian Mixture Models for Arabic Font Recognition (FS, SK, AMA, RI, JH), pp. 2174–2177.

ICPR-2010-StadelmannF #recognition

Dimension-Decoupled Gaussian Mixture Model for Short Utterance Speaker Recognition (TS, BF), pp. 1602–1605.

ICPR-2010-SuS #predict #process

Latent Fingerprint Core Point Prediction Based on Gaussian Processes (CS, SNS), pp. 1634–1637.

ICPR-2010-TanakaKO

Progressive MAP-based Deconvolution with Pixel-Dependent Gaussian Prior (MT, TK, MO), pp. 4428–4431.

ICPR-2010-WangJMS #process #using

Decoding Finger Flexion from Electrocorticographic Signals Using a Sparse Gaussian Process (ZW, QJ, KJM, GS), pp. 3756–3759.

ICPR-2010-WangM #learning #order #process #using

Gaussian Process Learning from Order Relationships Using Expectation Propagation (RW, SJM), pp. 605–608.

ICPR-2010-ZhangZZLL #classification #enterprise #kernel

Gaussian ERP Kernel Classifier for Pulse Waveforms Classification (DZ, WZ, DZ, YL, NL), pp. 2736–2739.

ICPR-2010-ZhangZZZ #classification #kernel #metric #using

Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel (DZ, WZ, DZ, HZ), pp. 29–32.

KDIR-2010-JunGRO #image #predict #process

Predicting Ground-based Aerosol Optical Depth with Satellite Images Via Gaussian Processes (GJ, JG, VR, ZO), pp. 370–375.

SAC-2010-GongWWLZY #using

Recognizing affect from non-stylized body motion using shape of Gaussian descriptors (LG, TW, CW, FL, FZ, XY), pp. 1203–1206.

DAC-2009-TakahashiYT #analysis #statistics

A Gaussian mixture model for statistical timing analysis (ST, YY, ST), pp. 110–115.

ICDAR-2009-ChenLJ #estimation #modelling #orthogonal #recognition

Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models for Handwritten Digit Recognition (XC, XL, YJ), pp. 1151–1155.

ICDAR-2009-DoA #recognition

Maximum Margin Training of Gaussian HMMs for Handwriting Recognition (TMTD, TA), pp. 976–980.

ICDAR-2009-WangH #classification #design #fault #modelling #parametricity #precise #using

Design Compact Recognizers of Handwritten Chinese Characters Using Precision Constrained Gaussian Models, Minimum Classification Error Training and Parameter Compression (YW, QH), pp. 36–40.

ICML-2009-AdamsMM #parametricity #process

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities (RPA, IM, DJCM), pp. 9–16.

ICML-2009-DeisenrothHH #process

Analytic moment-based Gaussian process filtering (MPD, MFH, UDH), pp. 225–232.

ICML-2009-JohnsonCC #representation

Orbit-product representation and correction of Gaussian belief propagation (JKJ, VYC, MC), pp. 473–480.

ICML-2009-LawrenceU #matrix #process

Non-linear matrix factorization with Gaussian processes (NDL, RU), pp. 601–608.

ICML-2009-MarlinM #modelling #visual notation

Sparse Gaussian graphical models with unknown block structure (BMM, KPM), pp. 705–712.

ICML-2009-Schmidt #process #using

Function factorization using warped Gaussian processes (MNS), pp. 921–928.

MLDM-2009-HasanG #adaptation #classification #modelling

Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier (BASH, JQG), pp. 96–106.

SAC-2009-BaechlerBH #image #modelling #using #verification

Labeled images verification using Gaussian mixture models (MB, JLB, JH), pp. 1331–1335.

SAC-2009-KontosB #modelling #network #visual notation

An improved shrinkage estimator to infer regulatory networks with Gaussian graphical models (KK, GB), pp. 793–798.

DAC-2008-WangLZTYTCN #scheduling

Timing yield driven clock skew scheduling considering non-Gaussian distributions of critical path delays (YW, WSL, XZ, JT, CY, JT, WC, JN), pp. 223–226.

CIKM-2008-SongZG #classification #framework #performance #process

A sparse gaussian processes classification framework for fast tag suggestions (YS, LZ, CLG), pp. 93–102.

ICML-2008-AdamsS #modelling #parametricity #process

Gaussian process product models for nonparametric nonstationarity (RPA, OS), pp. 1–8.

ICML-2008-CunninghamSS #estimation #performance #process

Fast Gaussian process methods for point process intensity estimation (JPC, KVS, MS), pp. 192–199.

ICML-2008-HyvarinenSH #modelling

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity (AH, SS, POH), pp. 424–431.

ICML-2008-WalderKS #multi #process

Sparse multiscale gaussian process regression (CW, KIK, BS), pp. 1112–1119.

ICML-2008-ZhangDT #algorithm

Estimating local optimums in EM algorithm over Gaussian mixture model (ZZ, BTD, AKHT), pp. 1240–1247.

ICPR-2008-BruneauGP #approach #modelling #reduction

Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach (PB, MG, FP), pp. 1–4.

ICPR-2008-GaoCY #modelling #process

Manifold denoising with Gaussian Process Latent Variable Models (YG, KLC, WYY), pp. 1–4.

ICPR-2008-HainesW #random #using

Combining shape-from-shading and stereo using Gaussian-Markov random fields (TSFH, RCW), pp. 1–4.

ICPR-2008-HaqueMP #adaptation #detection #generative #multi #robust

Improved Gaussian mixtures for robust object detection by adaptive multi-background generation (MH, MMM, MP), pp. 1–4.

ICPR-2008-LevadaMT #image #markov #modelling #on the #probability #random

On the asymptotic variances of Gaussian Markov Random Field model hyperparameters in stochastic image modeling (ALML, NDAM, AT), pp. 1–4.

ICPR-2008-LiDM #feature model #learning #locality #using

Localized feature selection for Gaussian mixtures using variational learning (YL, MD, YM), pp. 1–4.

ICPR-2008-PiccardiGO #classification #modelling #reduction

Maximum-likelihood dimensionality reduction in gaussian mixture models with an application to object classification (MP, HG, AFO), pp. 1–4.

ICPR-2008-RebecchiJ #on the #string

On the gaussian distribution of strings (SR, JMJ), pp. 1–4.

ICPR-2008-ZhouWS #classification #process #recognition #using

Human motion recognition using Gaussian Processes classification (HZ, LW, DS), pp. 1–4.

SIGIR-2008-GuiverS #learning #process #rank

Learning to rank with SoftRank and Gaussian processes (JG, ES), pp. 259–266.

DAC-2007-ChengXH #analysis #statistics

Non-Linear Statistical Static Timing Analysis for Non-Gaussian Variation Sources (LC, JX, LH), pp. 250–255.

DATE-2007-ZhangOSFKB #analysis #approach #named #parametricity #process

CMCal: an accurate analytical approach for the analysis of process variations with non-gaussian parameters and nonlinear functions (MZ, MO, DS, MF, HK, EB), pp. 243–248.

HCI-MIE-2007-JungKK #estimation #image #modelling #using

Human Pose Estimation Using a Mixture of Gaussians Based Image Modeling (DJJ, KSK, HJK), pp. 649–658.

ICML-2007-KerstingPPB #process

Most likely heteroscedastic Gaussian process regression (KK, CP, PP, WB), pp. 393–400.

ICML-2007-KrauseG #approach #learning #process

Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.

ICML-2007-LawrenceM #modelling #process

Hierarchical Gaussian process latent variable models (NDL, AJM), pp. 481–488.

ICML-2007-UrtasunD #classification #process

Discriminative Gaussian process latent variable model for classification (RU, TD), pp. 927–934.

ICML-2007-WangFH #modelling #multi #process

Multifactor Gaussian process models for style-content separation (JMW, DJF, AH), pp. 975–982.

MLDM-2007-YuZWZ #3d #image #retrieval

A Filter-Refinement Scheme for 3D Model Retrieval Based on Sorted Extended Gaussian Image Histogram (ZY, SZ, HSW, JZ), pp. 643–652.

DAC-2006-SinghS #analysis #component #correlation #independence #parametricity #statistics #using

Statistical timing analysis with correlated non-gaussian parameters using independent component analysis (JS, SSS), pp. 155–160.

DATE-2006-AbbaspourFP #analysis #statistics

Non-gaussian statistical interconnect timing analysis (SA, HF, MP), pp. 533–538.

ICML-2006-BanerjeeGdN #modelling #optimisation #visual notation

Convex optimization techniques for fitting sparse Gaussian graphical models (OB, LEG, Ad, GN), pp. 89–96.

ICML-2006-Carreira-Perpinan #clustering #parametricity #performance

Fast nonparametric clustering with Gaussian blurring mean-shift (MÁCP), pp. 153–160.

ICML-2006-RudaryS #modelling #predict #probability

Predictive linear-Gaussian models of controlled stochastic dynamical systems (MRR, SPS), pp. 777–784.

ICML-2006-SrebroSR #clustering

An investigation of computational and informational limits in Gaussian mixture clustering (NS, GS, STR), pp. 865–872.

ICML-2006-WingateS #kernel #linear #modelling #predict #probability

Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems (DW, SPS), pp. 1017–1024.

ICPR-v1-2006-LiL #probability

Probabilistic Image-Based Rendering with Gaussian Mixture Model (WL, BL), pp. 179–182.

ICPR-v1-2006-LinWZZ #optimisation

Continuous Optimization based-on Boosting Gaussian Mixture Model (BL, XW, RtZ, ZZ), pp. 1192–1195.

ICPR-v2-2006-BenaventRS #algorithm #modelling #named

EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models (APB, FER, JMS), pp. 451–455.

ICPR-v2-2006-ChenY #modelling #using #video

Exploiting High Dimensional Video Features Using Layered Gaussian Mixture Models (DC, JY), pp. 1078–1081.

ICPR-v2-2006-GrimHSP #approach #modelling #using

A Subspace Approach to Texture Modelling by Using Gaussian Mixtures (JG, MH, PS, PP), pp. 235–238.

ICPR-v2-2006-GuoQ #3d #learning

Learning and Inference of 3D Human Poses from Gaussian Mixture Modeled Silhouettes (FG, GQ), pp. 43–47.

ICPR-v2-2006-IlonenPKK #classification

Gaussian mixture pdf in one-class classification: computing and utilizing confidence values (JI, PP, JKK, HK), pp. 577–580.

ICPR-v2-2006-Lu #evaluation #similarity

Joint Distributions based on DFB and Gaussian Mixtures for Evaluation of Style Similarity among Paintings (XL), pp. 865–868.

ICPR-v3-2006-JiaZHW #classification

Gaussian Weighted Histogram Intersection for License Plate Classification (WJ, HZ, XH, QW), pp. 574–577.

ICPR-v3-2006-ScheundersB #component #image #multi #using

Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model (PS, SDB), pp. 754–757.

ICPR-v3-2006-SchlapbachB #identification #modelling #using

Off-lineWriter Identification Using Gaussian Mixture Models (AS, HB), pp. 992–995.

ICPR-v4-2006-LeilaC #performance #recognition #speech

Efficient Gaussian Mixture for Speech Recognition (LZ, GC), pp. 294–297.

SAC-2006-RouguiRAGM #documentation #modelling #retrieval #scalability #set

Hierarchical organization of a set of Gaussian mixture speaker models for scaling up indexing and retrieval in audio documents (JER, MR, DA, MG, JMM), pp. 1369–1373.

DAC-2005-ChangZNV #analysis #parametricity #statistics

Parameterized block-based statistical timing analysis with non-gaussian parameters, nonlinear delay functions (HC, VZ, SN, CV), pp. 71–76.

DAC-2005-ZhangCHGC #analysis #polynomial #statistics

Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model (LZ, WC, YH, JAG, CCPC), pp. 83–88.

DAC-2005-ZhanSLPNS #analysis #statistics

Correlation-aware statistical timing analysis with non-gaussian delay distributions (YZ, AJS, XL, LTP, DN, MS), pp. 77–82.

DATE-2005-BalakrishnanT #configuration management #linear #using

Reconfigurable Linear Decompressors Using Symbolic Gaussian Elimination (KJB, NAT), pp. 1130–1135.

ICML-2005-ChuG #learning #process

Preference learning with Gaussian processes (WC, ZG), pp. 137–144.

ICML-2005-EngelMM #learning #process

Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.

ICML-2005-GuestrinKS #process

Near-optimal sensor placements in Gaussian processes (CG, AK, APS), pp. 265–272.

ICML-2005-LeSC #process

Heteroscedastic Gaussian process regression (QVL, AJS, SC), pp. 489–496.

ICML-2005-YuTS #learning #multi #process

Learning Gaussian processes from multiple tasks (KY, VT, AS), pp. 1012–1019.

ICML-2004-AltunHS #classification #process #sequence

Gaussian process classification for segmenting and annotating sequences (YA, TH, AJS).

ICML-2004-GramacyLM #parametricity #process

Parameter space exploration with Gaussian process trees (RBG, HKHL, WGM).

ICPR-v1-2004-ColemanSH #design #scalability

A Systematic Design Procedure for Scalable Near-Circular Laplacian of Gaussian Operators (SAC, BWS, MGH), pp. 700–703.

ICPR-v2-2004-MaD #adaptation #classification #using #word

Adaptive Word Style Classification Using a Gaussian Mixture Model (HM, DSD), pp. 606–609.

ICPR-v2-2004-Zivkovic #adaptation

Improved Adaptive Gaussian Mixture Model for Background Subtraction (ZZ), pp. 28–31.

ICPR-v3-2004-BoughorbelKAA #energy

Gaussian Energy Functions for Registration without Correspondences (FB, AK, BRA, MAA), pp. 24–27.

ICPR-v3-2004-GimelfarbFE #linear

Expectation-Maximization for a Linear Combination of Gaussians (GLG, AAF, AEB), pp. 422–425.

ICPR-v3-2004-HaindlGSPK

A Gaussian Mixture-Based Colour Texture Model (MH, JG, PS, PP, MK), pp. 177–180.

ICPR-v3-2004-MoW #modelling #segmentation #using #video

Video Modelling and Segmentation Using Gaussian Mixture Models (XM, RW), pp. 854–857.

ICPR-v4-2004-OrtizMG #image

Gaussian Noise Elimination in Colour Images by Vector-Connected Filters (FO, FTM, PG), pp. 807–810.

ICPR-v4-2004-SinghMB #robust

Robust KLT Tracking with Gaussian and Laplacian of Gaussian Weighting Functions (MS, MKM, AB), pp. 661–664.

ICPR-v4-2004-WangT #modelling #recognition

Bayesian Face Recognition Based on Gaussian Mixture Models (XW, XT), pp. 142–145.

ICDAR-2003-GunterB #optimisation #word

Optimizing the Number of States, Training Iterations and Gaussians in an HMM-based Handwritten Word Recognizer (SG, HB), pp. 472–476.

ICEIS-v2-2003-TolbaMGA #algorithm #analysis #image #multi #segmentation #using

MR-Brain Image Segmentation Using Gaussian Multiresolution Analysis and the EM Algorithm (MFT, MGM, TFG, MAMS), pp. 165–170.

ICML-2003-EngelMM #approach #difference #learning #process

Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.

ICML-2003-Graepel #difference #equation #linear #process

Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations (TG), pp. 234–241.

ICML-2003-KlautauJO #classification #comparison #kernel #modelling

Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers (AK, NJ, AO), pp. 353–360.

ICML-2003-ZhuGL #learning #using

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.

SIGIR-2003-Hofmann #analysis #collaboration #probability #semantics

Collaborative filtering via gaussian probabilistic latent semantic analysis (TH), pp. 259–266.

VMCAI-2003-Monniaux #abstraction #using

Abstraction of Expectation Functions Using Gaussian Distributions (DM), pp. 161–173.

ICPR-v2-2002-ChangHHC #detection #effectiveness #modelling

Shadow Elimination for Effective Moving Object Detection with Gaussian Models (CJC, WFH, JWH, YSC), pp. 540–543.

ICPR-v2-2002-GibsonCT #abstraction #modelling #using #visual notation

Visual Abstraction of Wildlife Footage Using Gaussian Mixture Models and the Minimum Description Length Criterion (DPG, NWC, BTT), pp. 814–817.

ICPR-v2-2002-MinagawaUT

Region Extraction Based on Belief Propagation for Gaussian Model (AM, KU, NT), pp. 507–510.

ICPR-v2-2002-Vaswani #classification #linear #matrix

A Linear Classifier for Gaussian Class Conditional Distributions with Unequal Covariance Matrices (NV), pp. 60–63.

ICPR-v2-2002-WildenauerMB #approach

A Gradient-Based Eigenspace Approach to Dealing with Occlusions and Non-Gaussian Noise (HW, TM, HB), pp. 977–980.

ICDAR-2001-ZhangDZ #orthogonal #recognition

Offline Handwritten Character Recognition Based on Discriminative Training of Orthogonal Gaussian Mixture Model (RZ, XD, JZ), pp. 221–225.

STOC-2001-SanjeevK #learning

Learning mixtures of arbitrary gaussians (SA, RK), pp. 247–257.

ICPR-v1-2000-GrossYW #invariant #modelling #recognition

Growing Gaussian Mixture Models for Pose Invariant Face Recognition (RG, JY, AW), pp. 5088–5091.

ICPR-v1-2000-Wilson #modelling #multi #named

MGMM: Multiresolution Gaussian Mixture Models for Computer Vision (RW), pp. 1212–1215.

ICPR-v2-2000-Figueiredo #approximate #learning #on the

On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies (MATF), pp. 2618–2621.

ICPR-v2-2000-Sanchez-Reillo #geometry #modelling #pattern matching #pattern recognition #recognition

Hand Geometry Pattern Recognition through Gaussian Mixture Modeling (RSR), pp. 2937–2940.

ICPR-v3-2000-FukuiIIW #analysis #component #using

Sign of Gaussian Curvature from Eigen Plane Using Principal Components Analysis (SF, YI, AI, RJW), pp. 3807–3810.

ICPR-v3-2000-PelecanosMSC #modelling #verification

Vector Quantization Based Gaussian Modeling for Speaker Verification (JWP, SM, SS, VC), pp. 3298–3301.

ICPR-v3-2000-Silvan-CardenasE #information management

Optic-Flow Information Extraction with Directional Gaussian-Derivatives (JLSC, BER), pp. 3194–3197.

ICPR-1998-SardoK #complexity #estimation #using #validation

Model complexity validation for PDF estimation using Gaussian mixtures (LS, JK), pp. 195–197.

ICPR-1998-VlietYV #recursion

Recursive Gaussian derivative filters (LJvV, ITY, PWV), pp. 509–514.

DAC-1997-NaborsFCK #modelling

Lumped Interconnect Models Via Gaussian Quadrature (KN, TTF, HWC, KSK), pp. 40–45.

ICDAR-1997-FrankeGKM #classification #comparison #markov #modelling #polynomial #recognition

A Comparison of Gaussian Distribution and Polynomial Classifiers in a Hidden Markov Model Based System for the Recognition of Cursive Script (JF, JMG, AK, EM), pp. 515–518.

ICPR-1996-IdoATS #segmentation

Stimulus-driven segmentation by Gaussian functions (SI, SA, RT, MS), pp. 487–491.

ICPR-1996-JacksonA

Elliptical Gaussian filters (SAJ, NA), pp. 775–779.

ICPR-1996-Zhao0C #image #segmentation

Simplified Gaussian and mean curvatures to range image segmentation (CZ, DZ, YC), pp. 427–431.