Stem covari$ (all stems)

41 papers:

ICML-2015-IoffeS #network #normalisation

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (SI, CS), pp. 448–456.

ICML-c2-2014-WangL #estimation #metric #robust

Robust Inverse Covariance Estimation under Noisy Measurements (JKW, SdL), pp. 928–936.

ICML-c2-2014-WenYG #learning #nondeterminism #robust

Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (JW, CNY, RG), pp. 631–639.

ICML-c2-2014-YangLR14a #matrix

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments (EY, ACL, PDR), pp. 397–405.

ICPR-2014-YangLLYL #estimation #identification #modelling

Color Models and Weighted Covariance Estimation for Person Re-identification (YY, SL, ZL, DY, SZL), pp. 1874–1879.

ICPR-2014-ZhaiSDJ #multi

Covariance-Based PCA for Multi-size Data (MZ, FS, DD, NJ), pp. 1603–1608.

ICML-c3-2013-ShenderL #trade-off

Computation-Risk Tradeoffs for Covariance-Thresholded Regression (DS, JDL), pp. 756–764.

ICML-c3-2013-WuHG #modelling #multi

Dynamic Covariance Models for Multivariate Financial Time Series (YW, JMHL, ZG), pp. 558–566.

ICML-c3-2013-ZhangZWKYM #kernel

Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels (KZ, VWZ, QW, JTYK, QY, IM), pp. 388–395.

KDD-2013-LozanoJD #distance #estimation #matrix #multi #robust

Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance (ACL, HJ, XD), pp. 293–301.

ICML-2012-JanzaminA #composition #independence #markov

High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains (MJ, AA), p. 60.

ICML-2012-KolarX #consistency

Consistent Covariance Selection From Data With Missing Values (MK, EPX), p. 85.

ICML-2012-StulpS #adaptation #matrix #policy

Path Integral Policy Improvement with Covariance Matrix Adaptation (FS, OS), p. 201.

ICML-2012-YuS #analysis #kernel

Analysis of Kernel Mean Matching under Covariate Shift (YY, CS), p. 150.

ICPR-2012-AnoopMBBR #representation #set

Covariance profiles: A signature representation for object sets (AKA, AM, UDB, CB, KRR), pp. 2541–2544.

ICPR-2012-LiW #encoding #matrix #recognition #using

Iris recognition using ordinal encoding of Log-Euclidean covariance matrices (PL, GW), pp. 2420–2423.

ICPR-2012-ZhangFWZJ #image #scalability #using

Scalable image co-segmentation using color and covariance features (SZ, WF, LW, JZ, JJ), pp. 3708–3711.

KDD-2011-WangBB #analysis #component #matrix #multi

Common component analysis for multiple covariance matrices (HW, AB, DB), pp. 956–964.

ICML-2010-KolarPX #on the #parametricity

On Sparse Nonparametric Conditional Covariance Selection (MK, APP, EPX), pp. 559–566.

ICML-2010-Seeger #scalability

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

ICPR-2010-CaiTP #people

Matching Groups of People by Covariance Descriptor (YC, VT, MP), pp. 2744–2747.

ICPR-2010-SattiGCP #adaptation #human-computer #interface

A Covariate Shift Minimisation Method to Alleviate Non-stationarity Effects for an Adaptive Brain-Computer Interface (ARS, CG, DC, GP), pp. 105–108.

ICPR-2010-UekiSI #adaptation #estimation

Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation (KU, MS, YI), pp. 3400–3403.

ICML-2009-ChoiCW #markov #modelling #multi

Exploiting sparse Markov and covariance structure in multiresolution models (MJC, VC, ASW), pp. 177–184.

KDD-2009-SunPLCWLRY #estimation #mining

Mining brain region connectivity for alzheimer’s disease study via sparse inverse covariance estimation (LS, RP, JL, KC, TW, JL, ER, JY), pp. 1335–1344.

ICPR-2008-DonoserB #matrix #segmentation #using

Using covariance matrices for unsupervised texture segmentation (MD, HB), pp. 1–4.

ICPR-2008-PalaioB #adaptation #multi #using

Multi-object tracking using an adaptive transition model particle filter with region covariance data association (HP, JB), pp. 1–4.

ICPR-2008-WangH #case study #modelling #online #recognition

A study of semi-tied covariance modeling for online handwritten Chinese character recognition (YW, QH), pp. 1–5.

ICPR-2008-ZhangYLCG

A covariance-based method for dynamic background subtraction (SZ, HY, SL, XC, WG), pp. 1–4.

HIMI-IIE-2007-TaguchiAT #analysis

Information on the Causal Relationship Between Store kaizen and Store Features That Attract Customers by Covariance Structural Analysis (YT, YA, TT), pp. 973–982.

DATE-2005-KangPR #analysis #statistics #using

Statistical Timing Analysis using Levelized Covariance Propagation (KK, BCP, KR), pp. 764–769.

KDD-2004-ChilsonNWZ #correlation #matrix #parallel #robust

Parallel computation of high dimensional robust correlation and covariance matrices (JC, RTN, AW, RHZ), pp. 533–538.

SAC-2004-SurazhskyG #c++ #type safety

Type-safe covariance in C++ (VS, JYG), pp. 1496–1502.

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

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

KDD-2002-AlqallafKMZ #correlation #data mining #mining #robust #scalability

Scalable robust covariance and correlation estimates for data mining (FAA, KPK, RDM, RHZ), pp. 14–23.

TOOLS-PACIFIC-1999-SchmolitzkyEKM #how #question #type safety

How Can Covariance in Pragmatical Class Methods be Made Statically Type-Safe? (AS, ME, JLK, GM), pp. 200–209.

ICPR-1998-LiuH #recognition #using

Using centroid covariance in target recognition (GL, RMH), pp. 1343–1346.

ICPR-1998-NicollsJ #estimation

Maximum likelihood estimation of Toeplitz-block-Toeplitz covariances in the presence of subspace interference (FN, GdJ), pp. 1595–1597.

ECOOP-1996-BoylandC #compilation #type safety

Type-Safe Compilation of Covariant Specialization: A Practical Case (JB, GC), pp. 3–25.

DAC-1993-MotoharaHMMKSS #algorithm #matrix #traversal #using

A State Traversal Algorithm Using a State Covariance Matrix (AM, TH, MM, HM, KK, YS, SS), pp. 97–101.

TOOLS-USA-1992-Weber #correctness #how

Getting Class Correctness and System Correctness Equivalent (How to get covariance right) (FW), pp. 199–213.