Stem spars$ (all stems)

416 papers:

CASE-2015-JiangSLZ #database #estimation #physics

Physical field estimation from CFD database and sparse sensor observations (CJ, YCS, HL, HZ), pp. 1294–1299.

DATE-2015-ChenWY #parallel #performance

A fast parallel sparse solver for SPICE-based circuit simulators (XC, YW, HY), pp. 205–210.

DATE-2015-CilingirogluZUK #representation

Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits (TBC, MZ, AU, WCK, JK, AJ, BBG, MSÜ), pp. 597–600.

DATE-2015-WangHNYYWYZ #energy #in memory #recognition

An energy-efficient non-volatile in-memory accelerator for sparse-representation based face recognition (YW, HH, LN, HY, MY, CW, WY, JZ), pp. 932–935.

PLDI-2015-VenkatHS #data transformation #matrix

Loop and data transformations for sparse matrix code (AV, MWH, MS), pp. 521–532.

STOC-2015-BaconFHS #quantum

Sparse Quantum Codes from Quantum Circuits (DB, STF, AWH, JS), pp. 327–334.

STOC-2015-BansalGG #graph #independence #on the #set

On the Lovász Theta function for Independent Sets in Sparse Graphs (NB, AG, GG), pp. 193–200.

STOC-2015-BourgainDN #formal method #reduction #towards

Toward a Unified Theory of Sparse Dimensionality Reduction in Euclidean Space (JB, SD, JN), pp. 499–508.

STOC-2015-LovettZ #difference

Improved Noisy Population Recovery, and Reverse Bonami-Beckner Inequality for Sparse Functions (SL, JZ), pp. 137–142.

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-GrosseS #matrix #scalability

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix (RBG, RS), pp. 2304–2313.

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-JohnsonG #named #optimisation #scalability

Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization (TJ, CG), pp. 1171–1179.

ICML-2015-ManoelKTZ #approximate #estimation #message passing

Swept Approximate Message Passing for Sparse Estimation (AM, FK, EWT, LZ), pp. 1123–1132.

ICML-2015-Pouget-AbadieH #framework #graph

Inferring Graphs from Cascades: A Sparse Recovery Framework (JPA, TH), pp. 977–986.

ICML-2015-ShethWK #modelling

Sparse Variational Inference for Generalized GP Models (RS, YW, RK), pp. 1302–1311.

ICML-2015-SunLXB #clustering #multi

Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization (JS, JL, TX, JB), pp. 757–766.

ICML-2015-WangWS #analysis #clustering

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data (YW, YXW, AS), pp. 1422–1431.

ICML-2015-Yang0JZ15a #random #reduction

Theory of Dual-sparse Regularized Randomized Reduction (TY, LZ, RJ, SZ), pp. 305–314.

ICML-2015-YangRV #clustering

Sparse Subspace Clustering with Missing Entries (CY, DR, RV), pp. 2463–2472.

ICML-2015-YangX15a #analysis #component #streaming

Streaming Sparse Principal Component Analysis (WY, HX), pp. 494–503.

ICML-2015-YogatamaFDS #learning #word

Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.

ICML-2015-YouV #geometry

Geometric Conditions for Subspace-Sparse Recovery (CY, RV), pp. 1585–1593.

KDD-2015-FeldmanT #approximate #big data #constraints #matrix

More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data (DF, TT), pp. 249–258.

KDD-2015-QiATSA #predict

State-Driven Dynamic Sensor Selection and Prediction with State-Stacked Sparseness (GJQ, CA, DST, DMS, PA), pp. 945–954.

KDD-2015-ScheinPBW #multi

Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts (AS, JWP, DMB, HMW), pp. 1045–1054.

KDD-2015-WangFM #modelling

Trading Interpretability for Accuracy: Oblique Treed Sparse Additive Models (JW, RF, YM), pp. 1245–1254.

KDD-2015-WangYCSSZ #generative #named #recommendation

Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation (WW, HY, LC, YS, SWS, XZ), pp. 1255–1264.

MLDM-2015-KrasotkinaM #approach #optimisation #ranking

A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.

MLDM-2015-KrasotkinaM15a #analysis #approach

A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis (OK, VM), pp. 425–437.

SAC-2015-RochaRCOMVADGF #algorithm #classification #dataset #documentation #named #performance #using

G-KNN: an efficient document classification algorithm for sparse datasets on GPUs using KNN (LCdR, GSR, RC, RSO, DM, FV, GA, SD, MAG, RF), pp. 1335–1338.

CC-2015-DemangePS #coq #optimisation #performance #verification

Verifying Fast and Sparse SSA-Based Optimizations in Coq (DD, DP, LS), pp. 233–252.

CGO-2015-TangZLLHLG #multi #optimisation

Optimizing and auto-tuning scale-free sparse matrix-vector multiplication on Intel Xeon Phi (WTT, RZ, ML, YL, HPH, XL, RSMG), pp. 136–145.

SAT-2015-ChenS #algorithm #satisfiability

Improved Algorithms for Sparse MAX-SAT and MAX-k-CSP (RC, RS), pp. 33–45.

CASE-2014-ChenLY #distributed #modelling #network

Sparse particle filtering for modeling space-time dynamics in distributed sensor networks (YC, GL, HY), pp. 626–631.

DAC-2014-ApostolopoulouDES #matrix #scalability #simulation

Selective Inversion of Inductance Matrix for Large-Scale Sparse RLC Simulation (IA, KD, NEE, GIS), p. 6.

DAC-2014-SchaffnerGSKB #approximate #complexity #linear #realtime #video

An Approximate Computing Technique for Reducing the Complexity of a Direct-Solver for Sparse Linear Systems in Real-Time Video Processing (MS, FKG, AS, HK, LB), p. 6.

DAC-2014-WangOC #optimisation #performance #polynomial #synthesis

Enabling Efficient Analog Synthesis by Coupling Sparse Regression and Polynomial Optimization (YW, MO, CC), p. 6.

DATE-2014-RoyJ #named #optimisation #thread

ALLARM: Optimizing sparse directories for thread-local data (AR, TMJ), pp. 1–6.

DATE-2014-SarmaD #estimation #network #runtime

Minimal sparse observability of complex networks: Application to MPSoC sensor placement and run-time thermal estimation & tracking (SS, ND), pp. 1–6.

VLDB-2014-YanCZ #bound

Error-bounded Sampling for Analytics on Big Sparse Data (YY, LJC, ZZ), pp. 1508–1519.

FASE-2014-BersaniBGKP #smt

SMT-Based Checking of SOLOIST over Sparse Traces (MMB, DB, CG, SK, PSP), pp. 276–290.

SAS-2014-MadsenM #analysis #data flow #pointer #reachability

Sparse Dataflow Analysis with Pointers and Reachability (MM, AM), pp. 201–218.

STOC-2014-BerryCCKS #exponential #precise #simulation

Exponential improvement in precision for simulating sparse Hamiltonians (DWB, AMC, RC, RK, RDS), pp. 283–292.

ICALP-v1-2014-GilbertLPS

For-All Sparse Recovery in Near-Optimal Time (ACG, YL, EP, MJS), pp. 538–550.

CIKM-2014-GoncalvesDCSZB #learning #multi

Multi-task Sparse Structure Learning (ARG, PD, SC, VS, FJVZ, AB), pp. 451–460.

CIKM-2014-WangSZS #performance #scalability #semantics #similarity

Sparse Semantic Hashing for Efficient Large Scale Similarity Search (QW, BS, ZZ, LS), pp. 1899–1902.

ICML-c1-2014-LinX #adaptation #continuation #optimisation

An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization (QL, LX), pp. 73–81.

ICML-c2-2014-AnarakiH #memory management #performance #random

Memory and Computation Efficient PCA via Very Sparse Random Projections (FPA, SMH), pp. 1341–1349.

ICML-c2-2014-AsterisPD

Nonnegative Sparse PCA with Provable Guarantees (MA, DSP, AGD), pp. 1728–1736.

ICML-c2-2014-Cherian #nearest neighbour #using

Nearest Neighbors Using Compact Sparse Codes (AC), pp. 1053–1061.

ICML-c2-2014-QinLJ #learning #optimisation

Sparse Reinforcement Learning via Convex Optimization (ZQ, WL, FJ), pp. 424–432.

ICML-c2-2014-ReyRF

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

ICML-c2-2014-VinnikovS #component #independence

K-means recovers ICA filters when independent components are sparse (AV, SSS), pp. 712–720.

ICML-c2-2014-YangLR14a #matrix

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

ICML-c2-2014-Yi0WJJ #algorithm #clustering

A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data (JY, LZ, JW, RJ, AKJ), pp. 658–666.

ICPR-2014-AggarwalM #algorithm #metric #multi #random

Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors (HKA, AM), pp. 1014–1019.

ICPR-2014-AkhtarSM #algorithm #approximate #named

SUnGP: A Greedy Sparse Approximation Algorithm for Hyperspectral Unmixing (NA, FS, ASM), pp. 3726–3731.

ICPR-2014-BogunKCR #interactive #recognition #using

Interaction Recognition Using Sparse Portraits (IB, HK, JC, ER), pp. 2531–2536.

ICPR-2014-BorgiLEA14a #multi #recognition #using

Sparse Multi-regularized Shearlet-Network Using Convex Relaxation for Face Recognition (MAB, DL, ME, CBA), pp. 4636–4641.

ICPR-2014-ChoiCKD #distance #towards

Toward Sparse Coding on Cosine Distance (JC, HC, JK, LSD), pp. 4423–4428.

ICPR-2014-CoteA #segmentation

Sparseness-Based Descriptors for Texture Segmentation (MC, ABA), pp. 1108–1113.

ICPR-2014-DengHXC #analysis #composition #matrix #rank

Sparse and Low Rank Matrix Decomposition Based Local Morphological Analysis and Its Application to Diagnosis of Cirrhosis Livers (JD, XHH, GX, YWC), pp. 3363–3368.

ICPR-2014-DongSFBC #3d

Three-Dimensional Deconvolution of Wide Field Microscopy with Sparse Priors: Application to Zebrafish Imagery (BD, LS, AFF, OB, MDC), pp. 865–870.

ICPR-2014-FradiD #detection #recognition

Sparse Feature Tracking for Crowd Change Detection and Event Recognition (HF, JLD), pp. 4116–4121.

ICPR-2014-FuscoEM #data analysis #locality #network

Bad Data Analysis with Sparse Sensors for Leak Localisation in Water Distribution Networks (FF, BE, SM), pp. 3642–3647.

ICPR-2014-Kobayashi #named #pattern matching

S3CCA: Smoothly Structured Sparse CCA for Partial Pattern Matching (TK), pp. 1981–1986.

ICPR-2014-KusumotoHC #hybrid #recognition

Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition (RK, XHH, YWC), pp. 1490–1495.

ICPR-2014-LiHYGPJ #classification #re-engineering

Locality-Constrained Sparse Reconstruction for Trajectory Classification (CL, ZH, QY, SG, LP, JJ), pp. 2602–2606.

ICPR-2014-LitvinovL #incremental #modelling #visual notation

Incremental Solid Modeling from Sparse Structure-from-Motion Data with Improved Visual Artifacts Removal (VL, ML), pp. 2745–2750.

ICPR-2014-LiWQ #visual notation

Visual Tracking via Saliency Weighted Sparse Coding Appearance Model (WL, PW, HQ), pp. 4092–4097.

ICPR-2014-LuoYQY

Nuclear Norm Regularized Sparse Coding (LL, JY, JQ, JY), pp. 1834–1839.

ICPR-2014-LuoZYY #estimation

Region Tree Based Sparse Model for Optical Flow Estimation (WL, FZ, JY, JYY), pp. 2077–2082.

ICPR-2014-MoeiniMF #image #invariant #matrix #realtime #recognition

Real-Time Pose-Invariant Face Recognition by Triplet Pose Sparse Matrix from Only a Single Image (AM, HM, KF), pp. 465–470.

ICPR-2014-NayefGO #documentation #image

Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means (NN, PGK, JMO), pp. 4441–4446.

ICPR-2014-NegrelPG #image #learning #metric #performance #reduction #retrieval #using

Efficient Metric Learning Based Dimension Reduction Using Sparse Projectors for Image Near Duplicate Retrieval (RN, DP, PHG), pp. 738–743.

ICPR-2014-QuachDB #approximate #rank #recognition #representation #robust

Sparse Representation and Low-Rank Approximation for Robust Face Recognition (KGQ, CND, TDB), pp. 1330–1335.

ICPR-2014-SavakisRP #difference #gesture #learning #using

Gesture Control Using Active Difference Signatures and Sparse Learning (AES, RR, RWP), pp. 3969–3974.

ICPR-2014-SunTL #online #using

Sparse Online Co-regularization Using Conjugate Functions (BS, MT, GL), pp. 3666–3671.

ICPR-2014-WalhaDLGA #approach #image #learning #taxonomy

Sparse Coding with a Coupled Dictionary Learning Approach for Textual Image Super-resolution (RW, FD, FL, CG, AMA), pp. 4459–4464.

ICPR-2014-WangJ

Attribute Augmentation with Sparse Coding (XW, QJ), pp. 4352–4357.

ICPR-2014-WangZWB #learning #modelling

Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models (QW, XZ, MW, KLB), pp. 1987–1992.

ICPR-2014-XingY #categorisation #image #parametricity #representation #scalability

Large Scale Image Categorization in Sparse Nonparametric Bayesian Representation (SX, NHCY), pp. 1365–1370.

ICPR-2014-YanHCCC #classification #representation

PLSA-Based Sparse Representation for Object Classification (YY, JWH, HFC, SCC, DYC), pp. 1295–1300.

ICPR-2014-YanJY #feature model #representation

Sparse Representation Preserving for Unsupervised Feature Selection (HY, ZJ, JY), pp. 1574–1578.

ICPR-2014-ZhuYLL #3d #robust

Robust 3D Morphable Model Fitting by Sparse SIFT Flow (XZ, DY, ZL, SZL), pp. 4044–4049.

KDD-2014-ChenCW #classification #performance #scalability

Fast flux discriminant for large-scale sparse nonlinear classification (WC, YC, KQW), pp. 621–630.

KDD-2014-HoGS #health #named

Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization (JCH, JG, JS), pp. 115–124.

KDD-2014-LanSB #analysis #learning

Time-varying learning and content analytics via sparse factor analysis (ASL, CS, RGB), pp. 452–461.

KDD-2014-PurushothamMKO #feature model #higher-order #interactive #learning #modelling

Factorized sparse learning models with interpretable high order feature interactions (SP, MRM, CCJK, RO), pp. 552–561.

KDD-2014-VasishtDVK #classification #learning #multi

Active learning for sparse bayesian multilabel classification (DV, ACD, MV, AK), pp. 472–481.

KDD-2014-WangZQWD #multi #predict #risk management

Clinical risk prediction with multilinear sparse logistic regression (FW, PZ, BQ, XW, ID), pp. 145–154.

KDD-2014-WangZX #estimation #using

Travel time estimation of a path using sparse trajectories (YW, YZ, YX), pp. 25–34.

KDD-2014-ZhaoLC #performance

Safe and efficient screening for sparse support vector machine (ZZ, JL, JC), pp. 542–551.

KDD-2014-ZhouT #graph #multi

Multi-task copula by sparse graph regression (TZ, DT), pp. 771–780.

SIGIR-2014-ZhouDG #semantics #similarity

Latent semantic sparse hashing for cross-modal similarity search (JZ, GD, YG), pp. 415–424.

GPCE-2014-KaminGAXYC #multi #optimisation #runtime

Optimization by runtime specialization for sparse matrix-vector multiplication (SK, MJG, BA, DX, BY, ZC), pp. 93–102.

CC-2014-TavaresBPR #analysis #data flow

Parameterized Construction of Program Representations for Sparse Dataflow Analyses (ALCT, BB, FMQP, FR), pp. 18–39.

SAT-2014-SakaiST #reduction #satisfiability #strict

Solving Sparse Instances of Max SAT via Width Reduction and Greedy Restriction (TS, KS, ST), pp. 32–47.

DATE-2013-TengT #array #design #reduction

Sparse-rotary oscillator array (SROA) design for power and skew reduction (YT, BT), pp. 1229–1234.

DocEng-2013-DoTT #documentation #taxonomy #using

Document noise removal using sparse representations over learned dictionary (THD, ST, ORT), pp. 161–168.

ICDAR-2013-DoTT #approach #recognition #representation

New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation (THD, ST, ORT), pp. 265–269.

ICDAR-2013-KumarBTMNJ #documentation #image

Sparse Document Image Coding for Restoration (VK, AB, GHT, AM, AMN, CVJ), pp. 713–717.

ICDAR-2013-ShekharJ #documentation #retrieval #word

Document Specific Sparse Coding for Word Retrieval (RS, CVJ), pp. 643–647.

ICDAR-2013-SuTLDT #classification #documentation #image #learning #representation

Self Learning Classification for Degraded Document Images by Sparse Representation (BS, ST, SL, TAD, CLT), pp. 155–159.

ICDAR-2013-WalhaDLGA #clustering #image #multi

Multiple Learned Dictionaries Based Clustered Sparse Coding for the Super-Resolution of Single Text Image (RW, FD, FL, CG, AMA), pp. 484–488.

PODS-2013-Indyk #fourier #sketching

Sketching via hashing: from heavy hitters to compressed sensing to sparse fourier transform (PI), pp. 87–90.

VLDB-2013-0002GJ #correlation #markov #modelling #using

Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models (BY, CG, CSJ), pp. 769–780.

PLDI-2013-LiTCS #adaptation #multi #named

SMAT: an input adaptive auto-tuner for sparse matrix-vector multiplication (JL, GT, MC, NS), pp. 117–126.

STOC-2013-NikolovTZ #approximate #difference #geometry #privacy

The geometry of differential privacy: the sparse and approximate cases (AN, KT, LZ), pp. 351–360.

STOC-2013-RodittyW #algorithm #approximate #graph #performance

Fast approximation algorithms for the diameter and radius of sparse graphs (LR, VVW), pp. 515–524.

ICALP-v1-2013-BilleFGKSV

Sparse Suffix Tree Construction in Small Space (PB, JF, ILG, TK, BS, HWV), pp. 148–159.

ICALP-v1-2013-GilbertNPRS

ℓ2/ℓ2-Foreach Sparse Recovery with Low Risk (ACG, HQN, EP, AR, MJS), pp. 461–472.

HCI-III-2013-XuGC #classification #kernel #representation

Kernel Based Weighted Group Sparse Representation Classifier (BX, PG, CLPC), pp. 236–245.

CIKM-2013-ChanLKLBR #graph #matrix #using

Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation (JC, WL, AK, CL, JB, KR), pp. 811–816.

CIKM-2013-McDowellA #classification #network

Labels or attributes?: rethinking the neighbors for collective classification in sparsely-labeled networks (LM, DWA), pp. 847–852.

ICML-c1-2013-CotterSS #learning

Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.

ICML-c1-2013-HamiltonFP #modelling #predict

Modelling Sparse Dynamical Systems with Compressed Predictive State Representations (WLH, MMF, JP), pp. 178–186.

ICML-c1-2013-Jaggi #optimisation

Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization (MJ), pp. 427–435.

ICML-c1-2013-MehtaG #bound #predict

Sparsity-Based Generalization Bounds for Predictive Sparse Coding (NAM, AGG), pp. 36–44.

ICML-c1-2013-WangX #clustering

Noisy Sparse Subspace Clustering (YXW, HX), pp. 89–97.

ICML-c1-2013-XiangTY #feature model #optimisation #performance

Efficient Sparse Group Feature Selection via Nonconvex Optimization (SX, XT, JY), pp. 284–292.

ICML-c1-2013-ZhangC #analysis #linear

Sparse Uncorrelated Linear Discriminant Analysis (XZ, DC), pp. 45–52.

ICML-c2-2013-KyrillidisBCK

Sparse projections onto the simplex (ATK, SB, VC, CK), pp. 235–243.

ICML-c2-2013-MaurerPR #learning #multi

Sparse coding for multitask and transfer learning (AM, MP, BRP), pp. 343–351.

ICML-c3-2013-0002YY #linear

Guaranteed Sparse Recovery under Linear Transformation (JL, LY, JY), pp. 91–99.

ICML-c3-2013-BalasubramanianYL #learning

Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.

ICML-c3-2013-ChenCM #robust

Robust Sparse Regression under Adversarial Corruption (YC, CC, SM), pp. 774–782.

ICML-c3-2013-Cho #image

Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images (KC), pp. 432–440.

ICML-c3-2013-HockingRVB #detection #learning #using

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression (TH, GR, JPV, FRB), pp. 172–180.

ICML-c3-2013-HoXV #learning #on the #taxonomy

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning (JH, YX, BCV), pp. 1480–1488.

ICML-c3-2013-Kuleshov #algorithm #analysis #component #performance

Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration (VK), pp. 1418–1425.

ICML-c3-2013-PapailiopoulosDK #approximate #rank

Sparse PCA through Low-rank Approximations (DSP, AGD, SK), pp. 747–755.

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.

KDD-2013-0001Z

Exact sparse recovery with L0 projections (PL, CHZ), pp. 302–310.

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.

PPDP-2013-ODonnell #array #functional #parallel

Extensible sparse functional arrays with circuit parallelism (JTO), pp. 133–144.

DAC-2012-RenCWZY #gpu #parallel #simulation

Sparse LU factorization for parallel circuit simulation on GPU (LR, XC, YW, CZ, HY), pp. 1125–1130.

SIGMOD-2012-JinRXL #approach #distance #graph #query #scalability

A highway-centric labeling approach for answering distance queries on large sparse graphs (RJ, NR, YX, VEL), pp. 445–456.

PLDI-2012-OhHLLY #analysis #design #implementation

Design and implementation of sparse global analyses for C-like languages (HO, KH, WL, WL, KY), pp. 229–238.

STOC-2012-HassaniehIKP #fourier

Nearly optimal sparse fourier transform (HH, PI, DK, EP), pp. 563–578.

STOC-2012-LouisRTV

Many sparse cuts via higher eigenvalues (AL, PR, PT, SV), pp. 1131–1140.

ICALP-v1-2012-ChanLN #bound #fault tolerance #metric

Sparse Fault-Tolerant Spanners for Doubling Metrics with Bounded Hop-Diameter or Degree (THHC, ML, LN), pp. 182–193.

ICALP-v1-2012-GuptaN #approximate #integer #online #source code

Approximating Sparse Covering Integer Programs Online (AG, VN), pp. 436–448.

ICALP-v2-2012-ChandranGO #fault tolerance #network

Edge Fault Tolerance on Sparse Networks (NC, JAG, RO), pp. 452–463.

CIKM-2012-LongCZZ #classification #named #using

TCSST: transfer classification of short & sparse text using external data (GL, LC, XZ, CZ), pp. 764–772.

CIKM-2012-MehrotraAH #adaptation #representation #taxonomy

Dictionary based sparse representation for domain adaptation (RM, RA, SAH), pp. 2395–2398.

ICML-2012-BalakrishnanPL #functional #kernel

Sparse Additive Functional and Kernel CCA (SB, KP, JDL), p. 97.

ICML-2012-BelletHS #classification #learning #linear #similarity

Similarity Learning for Provably Accurate Sparse Linear Classification (AB, AH, MS), p. 193.

ICML-2012-BronsteinSS #learning #modelling #performance

Learning Efficient Structured Sparse Models (AMB, PS, GS), p. 33.

ICML-2012-Busa-FeketeBK #classification #graph #performance #using

Fast classification using sparse decision DAGs (RBF, DB, BK), p. 99.

ICML-2012-GoodfellowCB #learning #scalability

Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (IJG, ACC, YB), p. 180.

ICML-2012-Honorio #convergence #learning #modelling #optimisation #probability

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models (JH), p. 144.

ICML-2012-JanzaminA #composition #independence #markov

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

ICML-2012-LiuL #modelling #multi #named

Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling (YL, MTB, HL), p. 156.

ICML-2012-LozanoS #multi

Multi-level Lasso for Sparse Multi-task Regression (ACL, GS), p. 80.

ICML-2012-MerchanteGG #analysis #approach #linear #performance

An Efficient Approach to Sparse Linear Discriminant Analysis (LFSM, YG, GG), p. 168.

ICML-2012-MimnoHB #probability

Sparse stochastic inference for latent Dirichlet allocation (DMM, MDH, DMB), p. 197.

ICML-2012-MohamedHG #learning

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.

ICML-2012-Painter-WakefieldP #algorithm #learning

Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.

ICML-2012-Rakotomamonjy #infinity

Sparse Support Vector Infinite Push (AR), p. 48.

ICML-2012-SavalleRV #estimation #matrix #rank

Estimation of Simultaneously Sparse and Low Rank Matrices (PAS, ER, NV), p. 11.

ICML-2012-VaroquauxGT #clustering #correlation #design

Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering (GV, AG, BT), p. 178.

ICML-2012-XuL #multi

Conditional Sparse Coding and Grouped Multivariate Regression (MX, JDL), p. 116.

ICML-2012-YinCX #modelling

Group Sparse Additive Models (JY, XC, EPX), p. 214.

ICPR-2012-BaccoucheMWGB #2d #invariant #learning #recognition #representation #sequence

Sparse shift-invariant representation of local 2D patterns and sequence learning for human action recognition (MB, FM, CW, CG, AB), pp. 3823–3826.

ICPR-2012-ChangDZDW #representation #sketching #synthesis #using

Smoothness-constrained face photo-sketch synthesis using sparse representation (LC, XD, MZ, FD, ZW), pp. 3025–3029.

ICPR-2012-ChangWCYH #assessment #image #quality

Sparse feature fidelity for image quality assessment (HwC, MhW, SqC, HY, ZjH), pp. 1619–1622.

ICPR-2012-ChherawalaWC #documentation #reduction

Sparse descriptor for lexicon reduction in handwritten Arabic documents (YC, RW, MC), pp. 3729–3732.

ICPR-2012-ChowdhuryBP #detection #using

Scene text detection using sparse stroke information and MLP (ARC, UB, SKP), pp. 294–297.

ICPR-2012-DingLHXW #recognition #video

Context-aware horror video scene recognition via cost-sensitive sparse coding (XD, BL, WH, WX, ZW), pp. 1904–1907.

ICPR-2012-DoTT #multi #representation #using

Text/graphic separation using a sparse representation with multi-learned dictionaries (THD, ST, ORT), pp. 689–692.

ICPR-2012-DuanWLDC #learning #named

K-CPD: Learning of overcomplete dictionaries for tensor sparse coding (GD, HW, ZL, JD, YWC), pp. 493–496.

ICPR-2012-Filip

Restoring illumination and view dependent data from sparse samples (JF), pp. 1391–1394.

ICPR-2012-FuMW #estimation #representation

Night Removal by Color Estimation and sparse representation (HF, HM, SW), pp. 3656–3659.

ICPR-2012-GaoLZXX #recognition #representation

Human action recognition based on sparse representation induced by L1/L2 regulations (ZG, AL, HZ, GX, YX), pp. 1868–1871.

ICPR-2012-GuoRM #classification #representation #similarity

Similarity weighted sparse representation for classification (SG, QR, ZM), pp. 1241–1244.

ICPR-2012-GuoZC #representation

Find dominant bins of a histogram by sparse representation (XG, ZCZ, AC), pp. 3038–3041.

ICPR-2012-HanQC #adaptation #classification #image #representation

Group sparse representation of adaptive sub-domain selection for image classification (XHH, XQ, YWC), pp. 1431–1434.

ICPR-2012-HuangHHLJW #representation

Face hallucination via K-selection mean constrained sparse representation (KH, RH, ZH, TL, JJ, FW), pp. 882–885.

ICPR-2012-InoueSK #representation #using

Local intensity compensation using sparse representation (KI, HS, YK), pp. 951–954.

ICPR-2012-IshidaKKN #generative #recognition #using

Road marking recognition for map generation using sparse tensor voting (HI, KK, YK, TN), pp. 1132–1135.

ICPR-2012-IwamotoHSTXC #image #representation #self #using

Super-resolution of MR volumetric images using sparse representation and self-similarity (YI, XHH, SS, KT, WX, YWC), pp. 3758–3761.

ICPR-2012-JiS #clustering #robust #segmentation

Robust motion segmentation via refined sparse subspace clustering (HJ, FS), pp. 1546–1549.

ICPR-2012-KangLXP #classification #kernel #representation

Kernel Homotopy based sparse representation for object classification (CK, SL, SX, CP), pp. 1479–1482.

ICPR-2012-LinLZ #learning #representation #taxonomy

Incoherent dictionary learning for sparse representation (TL, SL, HZ), pp. 1237–1240.

ICPR-2012-LiuW12b #performance #recognition #robust

Accelerated robust sparse coding for fast face recognition (GL, YY, HW), pp. 3394–3397.

ICPR-2012-LiuWXZS #classification

Soft-signed sparse coding for ground-based cloud classification (SL, CW, BX, ZZ, YS), pp. 2214–2217.

ICPR-2012-LiZZZS #image #taxonomy

Blind image deblurring based on sparse prior of dictionary pair (HL, YZ, HZ, YZ, JS), pp. 3054–3057.

ICPR-2012-LuKT #recognition #representation

Action recognition via sparse representation of characteristic frames (GL, MK, JT), pp. 3268–3271.

ICPR-2012-OuYCPGJ #image #representation

Structured sparse coding for image representation based on L1-graph (WO, XY, YmC, QP, MG, XJ), pp. 3220–3223.

ICPR-2012-ParisHG #analysis #categorisation #image #towards

Sparse coding for histograms of local binary patterns applied for image categorization: Toward a Bag-of-Scenes analysis (SP, XH, HG), pp. 2817–2820.

ICPR-2012-RenLWG #image

Image super-resolution by structural sparse coding (JR, JL, MW, ZG), pp. 1936–1939.

ICPR-2012-SemenovichSG #modelling #predict

Predicting onsets of genocide with sparse additive models (DS, AS, BEG), pp. 3549–3552.

ICPR-2012-TakalaP #dataset #identification #named #network #people

CMV100: A dataset for people tracking and re-identification in sparse camera networks (VT, MP), pp. 1387–1390.

ICPR-2012-WangSCPZ #analysis #component #feature model #named

STPCA: Sparse tensor Principal Component Analysis for feature extraction (SW, MS, YHC, EPP, CZ), pp. 2278–2281.

ICPR-2012-WangSCSW #categorisation #representation

Object categorization via sparse representation of local features (JW, XS, RC, MFS, QW), pp. 3005–3008.

ICPR-2012-WangXY #classification #image #re-engineering

Sparse residue for occluded face image reconstruction and classification (JW, YX, JY), pp. 1707–1710.

ICPR-2012-WittW #programming #using

Sparse stereo by edge-based search using dynamic programming (JW, UW), pp. 3631–3635.

ICPR-2012-YamashitaTHNH #detection #representation

Sparse representation of audio features for sputum detection from lung sounds (TY, ST, KH, YN, SH), pp. 2005–2008.

ICPR-2012-YangLL #image

Color image fusion with extend joint sparse model (BY, JL, SL), pp. 376–379.

ICPR-2012-YiP #classification #graph

Sparse Granger causality graphs for human action classification (SY, VP), pp. 3374–3377.

ICPR-2012-ZhangXSS #modelling #process #recognition #representation #smarttech #using

Sparse representation for motion primitive-based human activity modeling and recognition using wearable sensors (MZ, WX, AAS, MS), pp. 1807–1810.

ICPR-2012-ZhangZC #recognition

Action recognition based on spatial-temporal pyramid sparse coding (XZ, HZ, XC), pp. 1455–1458.

ICPR-2012-ZhangZNH #learning #multi #recognition

Joint dynamic sparse learning and its application to multi-view face recognition (HZ, YZ, NMN, TSH), pp. 1671–1674.

ICPR-2012-ZhengQ #categorisation #semantics

Non-negative Sparse Semantic Coding for text categorization (WZ, YQ), pp. 409–412.

ICPR-2012-ZhuJL #image #representation

Optimized image super-resolution based on sparse representation (YZ, JJ, KL), pp. 1052–1055.

ICPR-2012-ZhuXWF #kernel #recognition #representation

Kernel based sparse representation for face recognition (QZ, YX, JW, ZF), pp. 1703–1706.

KDD-2012-LiuBEWFZ #comparison #mining #scalability

Mining large-scale, sparse GPS traces for map inference: comparison of approaches (XL, JB, JE, YW, GF, YZ), pp. 669–677.

KDD-2012-ShiA #dataset #mobile #recommendation

GetJar mobile application recommendations with very sparse datasets (KS, KA), pp. 204–212.

KDD-2012-SindhwaniG #distributed #learning #scalability #taxonomy

Large-scale distributed non-negative sparse coding and sparse dictionary learning (VS, AG), pp. 489–497.

KDD-2012-ZhouLNY #modelling

Modeling disease progression via fused sparse group lasso (JZ, JL, VAN, JY), pp. 1095–1103.

RecSys-2012-NingK #linear #recommendation

Sparse linear methods with side information for top-n recommendations (XN, GK), pp. 155–162.

SEKE-2012-WangXC #linear #mining #network #social

Sparse Linear Influence Model for Hot User Selection on Mining a Social Network (YW, GX, SKC), pp. 1–6.

ISSTA-2012-SuiYX #analysis #detection #memory management #using

Static memory leak detection using full-sparse value-flow analysis (YS, DY, JX), pp. 254–264.

DAC-2011-SunLT #analysis #approximate #grid #incremental #performance #power management

Efficient incremental analysis of on-chip power grid via sparse approximation (PS, XL, MYT), pp. 676–681.

VLDB-2011-YangPS #graph #mining #multi #performance

Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining (XY, SP, PS), pp. 231–242.

STOC-2011-IndykP #clustering #distance #modelling

K-median clustering, model-based compressive sensing, and sparse recovery for earth mover distance (PI, EP), pp. 627–636.

CIKM-2011-AktolgaA #query #ranking

Reranking search results for sparse queries (EA, JA), pp. 173–182.

CIKM-2011-LiuCZH #learning #random

Learning conditional random fields with latent sparse features for acronym expansion finding (JL, JC, YZ, YH), pp. 867–872.

CIKM-2011-QuC #probability

Sparse structured probabilistic projections for factorized latent spaces (XQ, XC), pp. 1389–1394.

ICML-2011-CoatesN #encoding

The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization (AC, AYN), pp. 921–928.

ICML-2011-DasK #algorithm #approximate #set #taxonomy

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection (AD, DK), pp. 1057–1064.

ICML-2011-EisensteinAX #generative #modelling

Sparse Additive Generative Models of Text (JE, AA, EPX), pp. 1041–1048.

ICML-2011-OrabonaL #algorithm #kernel #learning #multi #optimisation

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning (FO, JL), pp. 249–256.

ICML-2011-ZhangDC #infinity

Tree-Structured Infinite Sparse Factor Model (XZ, DBD, LC), pp. 785–792.

ICML-2011-ZhongK #automation #modelling #performance

Efficient Sparse Modeling with Automatic Feature Grouping (WZ, JTK), pp. 9–16.

ICML-2011-ZhouT #composition #matrix #named #random

GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case (TZ, DT), pp. 33–40.

KDD-2011-ChenZY #learning #multi #rank #robust

Integrating low-rank and group-sparse structures for robust multi-task learning (JC, JZ, JY), pp. 42–50.

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.

KDD-2011-JiangFH #data type #graph #locality #network

Anomaly localization for network data streams with graph joint sparse PCA (RJ, HF, JH), pp. 886–894.

MLDM-2011-XuM #learning #taxonomy

Dictionary Learning Based on Laplacian Score in Sparse Coding (JX, HM), pp. 253–264.

PPoPP-2011-MurarasuWBBP #algorithm #data type #grid #scalability

Compact data structure and scalable algorithms for the sparse grid technique (AFM, JW, GB, DB, DP), pp. 25–34.

DAC-2010-ZhangCTL #modelling #multi #performance #scalability #towards

Toward efficient large-scale performance modeling of integrated circuits via multi-mode/multi-corner sparse regression (WZ, THC, MYT, XL), pp. 897–902.

STOC-2010-BayatiGT #approach #combinator #graph #random #scalability

Combinatorial approach to the interpolation method and scaling limits in sparse random graphs (MB, DG, PT), pp. 105–114.

STOC-2010-DasguptaKS

A sparse Johnson: Lindenstrauss transform (AD, RK, TS), pp. 341–350.

STOC-2010-GilbertLPS #approximate #metric #optimisation

Approximate sparse recovery: optimizing time and measurements (ACG, YL, EP, MJS), pp. 475–484.

ICALP-v2-2010-ChandranGO #fault tolerance #network

Improved Fault Tolerance and Secure Computation on Sparse Networks (NC, JAG, RO), pp. 249–260.

ICALP-v2-2010-ChechikEPP #graph #reliability

Sparse Reliable Graph Backbones (SC, YE, BPS, DP), pp. 261–272.

ICFP-2010-ArnoldHKBS #matrix #specification #verification

Specifying and verifying sparse matrix codes (GA, JH, ASK, RB, MS), pp. 249–260.

ICML-2010-GregorL #approximate #learning #performance

Learning Fast Approximations of Sparse Coding (KG, YL), pp. 399–406.

ICML-2010-JenattonMOB #learning #taxonomy

Proximal Methods for Sparse Hierarchical Dictionary Learning (RJ, JM, GO, FRB), pp. 487–494.

ICML-2010-KolarPX #on the #parametricity

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

ICML-2010-KrauseC #representation #taxonomy

Submodular Dictionary Selection for Sparse Representation (AK, VC), pp. 567–574.

ICML-2010-TanWT #dataset #feature model #learning

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.

ICML-2010-ThiaoTA #approach #problem #programming

A DC Programming Approach for Sparse Eigenvalue Problem (MT, PDT, LTHA), pp. 1063–1070.

ICML-2010-YanQ #process

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

ICPR-2010-BuyssensR #learning #verification

Learning Sparse Face Features: Application to Face Verification (PB, MR), pp. 670–673.

ICPR-2010-ChangZHD #representation #sketching #synthesis

Face Sketch Synthesis via Sparse Representation (LC, MZ, YH, XD), pp. 2146–2149.

ICPR-2010-DaiYW #classification #image

Three-layer Spatial Sparse Coding for Image Classification (DD, WY, TW), pp. 613–616.

ICPR-2010-GhanemA #linear #recognition

Sparse Coding of Linear Dynamical Systems with an Application to Dynamic Texture Recognition (BG, NA), pp. 987–990.

ICPR-2010-KhwajaAG #invariant #recognition #representation #using

Illumination and Expression Invariant Recognition Using SSIM Based Sparse Representation (AAK, AA, RG), pp. 4028–4031.

ICPR-2010-KotropoulosAP #classification #music

Ensemble Discriminant Sparse Projections Applied to Music Genre Classification (CK, GRA, YP), pp. 822–825.

ICPR-2010-LaiJYW #feature model

Sparse Local Discriminant Projections for Feature Extraction (ZL, ZJ, JY, WKW), pp. 926–929.

ICPR-2010-LiGZ #classification #representation

Local Sparse Representation Based Classification (CGL, JG, HGZ), pp. 649–652.

ICPR-2010-LiuZZZ #representation

Fingerprint Pore Matching Based on Sparse Representation (FL, QZ, LZ, DZ), pp. 1630–1633.

ICPR-2010-NaseemTB10a #identification #representation

Sparse Representation for Speaker Identification (IN, RT, MB), pp. 4460–4463.

ICPR-2010-PapalazarouRW #detection #estimation #image #multi #using

Multiple Model Estimation for the Detection of Curvilinear Segments in Medical X-ray Images Using Sparse-plus-dense-RANSAC (CP, PMJR, PHNdW), pp. 2484–2487.

ICPR-2010-QiuPVLL #performance #recognition #representation #robust

A Fast Extension for Sparse Representation on Robust Face Recognition (HQ, DSP, SV, WL, JHL), pp. 1023–1027.

ICPR-2010-RahtuSH #random #using

Compressing Sparse Feature Vectors Using Random Ortho-Projections (ER, MS, JH), pp. 1397–1400.

ICPR-2010-RaraAESF #re-engineering #recognition #using

Face Recognition at-a-Distance Using Texture, Dense- and Sparse-Stereo Reconstruction (HMR, AMA, SYE, TLS, AAF), pp. 1221–1224.

ICPR-2010-SongLLJ #detection #representation

A Discriminative Model for Object Representation and Detection via Sparse Features (XS, PL, LL, YJ), pp. 3077–3080.

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-WuLSZ #modelling #word

Integrating ILSR to Bag-of-Visual Words Model Based on Sparse Codes of SIFT Features Representations (LW, SL, WS, XZ), pp. 4283–4286.

ICPR-2010-YangC #classification #representation

Sparse Representation Classifier Steered Discriminative Projection (JY, DC), pp. 694–697.

ICPR-2010-ZhangYFZ #on the #recognition #reduction #representation

On the Dimensionality Reduction for Sparse Representation Based Face Recognition (LZ, MY, ZF, DZ), pp. 1237–1240.

ICPR-2010-ZhangZYK #classification #detection #learning #representation #taxonomy

Microaneurysm (MA) Detection via Sparse Representation Classifier with MA and Non-MA Dictionary Learning (BZ, LZ, JY, FK), pp. 277–280.

ICPR-2010-ZhaoLLY #visual notation

Sparse Embedding Visual Attention Systems Combined with Edge Information (CZ, CL, ZL, JY), pp. 3432–3435.

KDD-2010-ChenLY #learning #multi #rank

Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.

KDD-2010-LiuMTLL #learning #metric #optimisation #using

Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.

SIGIR-2010-ArvolaKJ #documentation

Focused access to sparsely and densely relevant documents (PA, JK, MJ), pp. 781–782.

SAC-2010-ChandaFP #classification #documentation

Structural handwritten and machine print classification for sparse content and arbitrary oriented document fragments (SC, KF, UP), pp. 18–22.

PPoPP-2010-ChoiSV #modelling #multi

Model-driven autotuning of sparse matrix-vector multiply on GPUs (JC, AS, RWV), pp. 115–126.

PPoPP-2010-HoeflerSL #communication #protocol #scalability

Scalable communication protocols for dynamic sparse data exchange (TH, CS, AL), pp. 159–168.

ICDAR-2009-LiuBMG #bound #detection #fault #sequence

Improving the Table Boundary Detection in PDFs by Fixing the Sequence Error of the Sparse Lines (YL, KB, PM, CLG), pp. 1006–1010.

ICDAR-2009-PanBS #recognition #using

Isolated Handwritten Farsi Numerals Recognition Using Sparse and Over-Complete Representations (WP, TDB, CYS), pp. 586–590.

STOC-2009-AndersenP #evolution #set #using

Finding sparse cuts locally using evolving sets (RA, YP), pp. 235–244.

CIAA-2009-KutribM #automaton #communication

Cellular Automata with Sparse Communication (MK, AM), pp. 34–43.

ICALP-v1-2009-DurandRS #complexity #fault

High Complexity Tilings with Sparse Errors (BD, AER, AS), pp. 403–414.

CIKM-2009-TangL #behaviour #learning #scalability #social

Scalable learning of collective behavior based on sparse social dimensions (LT, HL), pp. 1107–1116.

ICML-2009-ChoiCW #markov #modelling #multi

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

ICML-2009-GargK #algorithm #strict

Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property (RG, RK), pp. 337–344.

ICML-2009-MairalBPS #learning #online #taxonomy

Online dictionary learning for sparse coding (JM, FRB, JP, GS), pp. 689–696.

ICML-2009-MarlinM #modelling #visual notation

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

ICML-2009-QianJZHW #higher-order #random #sequence

Sparse higher order conditional random fields for improved sequence labeling (XQ, XJ, QZ, XH, LW), pp. 849–856.

ICML-2009-QiTZCZ #learning #metric #performance

An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization (GJQ, JT, ZJZ, TSC, HJZ), pp. 841–848.

KDD-2009-DaruruMWG #clustering #data flow #data mining #mining #parallel #pervasive #scalability

Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data (SD, NMM, MW, JG), pp. 1115–1124.

KDD-2009-JiYLZKY #interactive #using

Drosophila gene expression pattern annotation using sparse features and term-term interactions (SJ, LY, YXL, ZHZ, SK, JY), pp. 407–416.

KDD-2009-LiuCY #scalability

Large-scale sparse logistic regression (JL, JC, JY), pp. 547–556.

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.

KDD-2009-ZhuXZ #markov #network

Primal sparse Max-margin Markov networks (JZ, EPX, BZ), pp. 1047–1056.

SIGIR-2009-LinYCWW #approach #modelling #semantics #thread

Simultaneously modeling semantics and structure of threaded discussions: a sparse coding approach and its applications (CL, JMY, RC, XJW, WW, LZ), pp. 131–138.

SIGIR-2009-SunQTW #learning #metric #rank #ranking #robust

Robust sparse rank learning for non-smooth ranking measures (ZS, TQ, QT, JW), pp. 259–266.

POPL-2009-HardekopfL #analysis #pointer

Semi-sparse flow-sensitive pointer analysis (BH, CL), pp. 226–238.

SAC-2009-ShenU #approximate #composition #concept #matrix #multi

A class of multistep sparse matrix strategies for concept decomposition matrix approximation (CS, MU), pp. 1714–1718.

PPoPP-2009-KangB #algorithm #graph #memory management #performance #transaction

An efficient transactional memory algorithm for computing minimum spanning forest of sparse graphs (SK, DAB), pp. 15–24.

DAC-2008-Garland #gpu #manycore #matrix

Sparse matrix computations on manycore GPU’s (MG), pp. 2–6.

DRR-2008-Schomaker #mining #word

Word mining in a sparsely labeled handwritten collection (LRBS), p. 68150.

DRR-2008-ZouLT #html #online #using

Extracting a sparsely located named entity from online HTML medical articles using support vector machine (JZ, DXL, GRT), p. 68150.

STOC-2008-BenjaminiSS #graph

Every minor-closed property of sparse graphs is testable (IB, OS, AS), pp. 393–402.

ICALP-A-2008-BlellochVW #approach #combinator #graph #problem

A New Combinatorial Approach for Sparse Graph Problems (GEB, VV, RW), pp. 108–120.

ICALP-A-2008-CheboluFM #graph #random

Finding a Maximum Matching in a Sparse Random Graph in O(n) Expected Time (PC, AMF, PM), pp. 161–172.

ICALP-A-2008-DiakonikolasLMSW #testing

Efficiently Testing Sparse GF(2) Polynomials (ID, HKL, KM, RAS, AW), pp. 502–514.

ICALP-A-2008-DraganFG #graph

Spanners in Sparse Graphs (FFD, FVF, PAG), pp. 597–608.

CIKM-2008-LiuMG #bound #detection #documentation #identification

Identifying table boundaries in digital documents via sparse line detection (YL, PM, CLG), pp. 1311–1320.

CIKM-2008-ModaniD #clique #graph #scalability

Large maximal cliques enumeration in sparse graphs (NM, KD), pp. 1377–1378.

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-CaronD #parametricity

Sparse Bayesian nonparametric regression (FC, AD), pp. 88–95.

ICML-2008-SiggB

Expectation-maximization for sparse and non-negative PCA (CDS, JMB), pp. 960–967.

ICML-2008-WalderKS #multi #process

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

ICPR-2008-GurumoorthyRBR #image #representation

Beyond SVD: Sparse projections onto exemplar orthonormal bases for compact image representation (KSG, AR, AB, AR), pp. 1–4.

ICPR-2008-KuksaHP #detection #kernel #performance

Fast protein homology and fold detection with sparse spatial sample kernels (PPK, PHH, VP), pp. 1–4.

ICPR-2008-LecellierFJRA #segmentation

Region-based active contours and sparse representations for texture segmentation (FL, JF, SJB, MR, GA), pp. 1–4.

ICPR-2008-MazharG #design #named #taxonomy

EK-SVD: Optimized dictionary design for sparse representations (RM, PDG), pp. 1–4.

ICPR-2008-PanBS #detection #image #representation #using

Text detection from scene images using sparse representation (WP, TDB, CYS), pp. 1–5.

ICPR-2008-QuQY #learning

Learning a discriminative sparse tri-value transform (ZQ, GQ, PCY), pp. 1–4.

KDD-2008-GallagherTEF #classification #network #using

Using ghost edges for classification in sparsely labeled networks (BG, HT, TER, CF), pp. 256–264.

DATE-2007-ZhuZCXZ #grid #probability #process

A sparse grid based spectral stochastic collocation method for variations-aware capacitance extraction of interconnects under nanometer process technology (HZ, XZ, WC, JX, DZ), pp. 1514–1519.

ICDAR-2007-PanBS #segmentation #using

Text Segmentation from Complex Background Using Sparse Representations (WP, TDB, CYS), pp. 412–416.

ICDAR-2007-RanzatoL #documentation #image #invariant

A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images (MR, YL), pp. 1213–1217.

ICDAR-2007-WeinmanLH #performance #recognition

Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation (JJW, EGLM, ARH), pp. 979–983.

SIGMOD-2007-ChuBN #approach #relational #set

The case for a wide-table approach to manage sparse relational data sets (EC, JLB, JFN), pp. 821–832.

ICALP-2007-Elkin #algorithm #maintenance #streaming

Streaming and Fully Dynamic Centralized Algorithms for Constructing and Maintaining Sparse Spanners (ME), pp. 716–727.

DHM-2007-ZhengB #approach #modelling #navigation

An Integrated Approach for Reconstructing Surface Models of the Proximal Femur from Sparse Input Data for Surgical Navigation (GZ, MÁGB), pp. 767–775.

ICML-2007-BunescuM #learning #multi

Multiple instance learning for sparse positive bags (RCB, RJM), pp. 105–112.

ICML-2007-ChanVL

Direct convex relaxations of sparse SVM (ABC, NV, GRGL), pp. 145–153.

ICML-2007-dAspremontBG #analysis #component

Full regularization path for sparse principal component analysis (Ad, FRB, LEG), pp. 177–184.

ICML-2007-HeraultG #classification #probability

Sparse probabilistic classifiers (RH, YG), pp. 337–344.

ICML-2007-KropotovV #learning #on the

On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.

ICML-2007-SriperumbudurTL #programming

Sparse eigen methods by D.C. programming (BKS, DAT, GRGL), pp. 831–838.

KDD-2007-Li #random #reduction

Very sparse stable random projections for dimension reduction in lalpha (0 &lt;alpha<=2) norm (PL0), pp. 440–449.

SIGIR-2007-BalogBARB #retrieval

Broad expertise retrieval in sparse data environments (KB, TB, LA, MdR, AvdB), pp. 551–558.

SAC-2007-TreeprapinKHN #mobile #network

A mobile sensor control method for sparse sensor networks (KT, AK, TH, SN), pp. 886–890.

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-Haffner #kernel #learning #performance

Fast transpose methods for kernel learning on sparse data (PH), pp. 385–392.

ICML-2006-MoghaddamWA #bound

Generalized spectral bounds for sparse LDA (BM, YW, SA), pp. 641–648.

ICPR-v1-2006-OngB #clustering #learning

Learning Wormholes for Sparsely Labelled Clustering (EJO, RB), pp. 916–919.

ICPR-v1-2006-WongC #classification #gesture #recognition #using

Continuous Gesture Recognition using a Sparse Bayesian Classifier (SFW, RC), pp. 1084–1087.

ICPR-v2-2006-YuanQYZ #approach #classification #kernel

An Approach for Constructing Sparse Kernel Classifier (ZY, YQ, YY, NZ), pp. 560–563.

ICPR-v3-2006-KostlerPRH #adaptation

Adaptive variational sinogram interpolation of sparsely sampled CT data (HK, MP, UR, JH), pp. 778–781.

ICPR-v3-2006-MaKKLK #estimation

Sparse Bayesian Regression for Head Pose Estimation (YM, YK, KK, SL, MK), pp. 507–510.

ICPR-v3-2006-TangelderS #image #learning #multi #online #recognition #representation

Learning a Sparse Representation from Multiple Still Images for On-Line Face Recognition in an Unconstrained Environment (JWHT, BAMS), pp. 1087–1090.

ICPR-v3-2006-WongWC #classification #robust #using

Robust Appearance-based Tracking using a sparse Bayesian classifier (SFW, KYKW, RC), pp. 47–50.

KDD-2006-BiPOKFSR #classification #detection #symmetry

Computer aided detection via asymmetric cascade of sparse hyperplane classifiers (JB, SP, KO, TK, GF, MS, RBR), pp. 837–844.

KDD-2006-LiHC #random

Very sparse random projections (PL, TH, KWC), pp. 287–296.

KDD-2006-RosalesF #learning #linear #metric #programming

Learning sparse metrics via linear programming (RR, GF), pp. 367–373.

IJCAR-2006-LahiriM #constraints #linear

Solving Sparse Linear Constraints (SKL, MM), pp. 468–482.

DAC-2005-JiangCC #3d #algorithm #linear #named #order

ICCAP: a linear time sparse transformation and reordering algorithm for 3D BEM capacitance extraction (RJ, YHC, CCPC), pp. 163–166.

ICML-2005-WangLBS #online #optimisation

Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.

ICML-2005-WuSB #classification #scalability

Building Sparse Large Margin Classifiers (MW, BS, GHB), pp. 996–1003.

KDD-2005-Momma #kernel #performance #scalability

Efficient computations via scalable sparse kernel partial least squares and boosted latent features (MM), pp. 654–659.

DAC-2004-YanSS #3d #multi

Sparse transformations and preconditioners for hierarchical 3-D capacitance extraction with multiple dielectrics (SY, VS, WS), pp. 788–793.

ICALP-2004-ArgeMT #algorithm #graph #memory management

External Memory Algorithms for Diameter and All-Pairs Shortest-Paths on Sparse Graphs (LA, UM, LT), pp. 146–157.

ICML-2004-GoldenbergM #learning #scalability

Tractable learning of large Bayes net structures from sparse data (AG, AWM).

ICML-2004-KokV

Sparse cooperative Q-learning (JRK, NAV).

ICPR-v1-2004-LevesqueD

Sparse Scene Structure Recovery from Atmospheric Degradation (DL, FD), pp. 84–87.

ICPR-v1-2004-Martin-MerinoM #algorithm #visualisation

A New Sammon Algorithm for Sparse Data Visualization (MMM, AM), pp. 477–481.

ICPR-v3-2004-LiMH

New Method for Sparse Point-Sets Matching with Underlying Non-Rigidity (BL, QM, HH), pp. 8–11.

ICPR-v3-2004-RexhepiMR #modelling

Sparse, Variable-Representation Active Contour Models (AR, FM, AR), pp. 683–686.

ICPR-v4-2004-UlusoyHH #probability

Probabilistic Phase Based Sparse Stereo (IU, UH, ERH), pp. 84–87.

PLDI-2002-Gargi #algorithm

A Sparse Algorithm for Predicated Global Value Numbering (KG), pp. 45–56.

STOC-2002-ColeH #verification

Verifying candidate matches in sparse and wildcard matching (RC, RH), pp. 592–601.

STOC-2002-GilbertGIMS #fourier

Near-optimal sparse fourier representations via sampling (ACG, SG, PI, SM, MS), pp. 152–161.

ICALP-2002-Pettie #algorithm #graph #performance

A Faster All-Pairs Shortest Path Algorithm for Real-Weighted Sparse Graphs (SP), pp. 85–97.

ICML-2002-ThamDR #classification #learning #markov #monte carlo #using

Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo (SST, AD, KR), pp. 634–641.

ICPR-v2-2002-YangP02a #re-engineering

High-Resolution Reconstruction of Sparse Data from Dense Low-Resolution Spatio-Temporal Data (QY, BP), pp. 261–264.

STOC-2001-Shparlinski #approximate #finite #polynomial

Sparse polynomial approximation in finite fields (IS), pp. 209–215.

ICALP-2001-Thorup #graph

Quick k-Median, k-Center, and Facility Location for Sparse Graphs (MT), pp. 249–260.

ICML-2001-NairCK #algorithm

Some Greedy Algorithms for Sparse Nonlinear Regression (PBN, AC, AJK), pp. 369–376.

ICML-2001-Zhang #approximate #bound #problem

Some Sparse Approximation Bounds for Regression Problems (TZ0), pp. 624–631.

KDD-2001-GarckeG #data mining #mining #using

Data mining with sparse grids using simplicial basis functions (JG, MG), pp. 87–96.

STOC-2000-FederMS #graph

Finding long paths and cycles in sparse Hamiltonian graphs (TF, RM, CSS), pp. 524–529.

ICML-2000-SmolaS #approximate #machine learning #matrix

Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.

ICPR-v2-2000-KabanG #clustering #documentation

Initialized and Guided EM-Clustering of Sparse Binary Data with Application to Text Based Documents (AK, MG), pp. 2744–2747.

ICPR-v2-2000-NaphadeCHF #learning #modelling #multi

Learning Sparse Multiple Cause Models (MRN, LSC, TSH, BJF), pp. 2642–2647.

POPL-2000-RuthingKS

Sparse Code Motion (OR, JK, BS), pp. 170–183.

SAC-2000-HashemiETY #information management

Knowledge Discovery from Sparse Pharmacokinetic Data (RRH, CE, AAT, JFY), pp. 75–79.

SAC-2000-LeeKHL #dependence #graph #scheduling #using

Task Scheduling using a Block Dependency DAG for Block-Oriented Sparse Cholesky Factorization (HL, JK, SJH, SL), pp. 641–648.

SIGMOD-1999-BeyerR #bottom-up

Bottom-Up Computation of Sparse and Iceberg CUBEs (KSB, RR), pp. 359–370.

SIGMOD-1999-VitterW #approximate #multi #using

Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets (JSV, MW), pp. 193–204.

IWPC-1999-KesslerS #approach #automation #comprehension #matrix

The SPARAMAT Approach to Automatic Comprehension of Sparse Matrix Computations (CWK, CS), pp. 200–207.

WIA-1999-Kiraz #finite #transducer

Compressed Storage of Sparse Finite-State Transducers (GAK), pp. 109–121.

ICEIS-1999-ValeSFMRM #named

SPARSE-IT: An Intelligent Tutor for Power System Control Center Operator Training (ZAV, AS, LF, NM, CR, AM), pp. 327–334.

ICML-1999-Meila #algorithm

An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data (MM), pp. 249–257.

MLDM-1999-ReczkoKMGO #estimation #image #network

Neural Networks in MR Image Estimation from Sparsely Sampled Scans (MR, DAK, VM, DGD, DvO), pp. 75–86.

SAS-1998-SarkarK #array #constant

Enabling Sparse Constant Propagation of Array Elements via Array SSA Form (VS, KK), pp. 33–56.

SAS-1998-TadjouddineEF #automation #difference #program analysis

Sparse Jacobian Computation in Automatic Differentiation by Static Program Analysis (MT, FE, CF), pp. 311–326.

ICPR-1998-HyvarinenOHH #analysis #component #feature model #image #independence

Image feature extraction by sparse coding and independent component analysis (AH, EO, POH, JH), pp. 1268–1273.

DAC-1997-HeCP #named

SPIE: Sparse Partial Inductance Extraction (ZH, MC, LTP), pp. 137–140.

VLDB-1997-RossS #performance

Fast Computation of Sparse Datacubes (KAR, DS), pp. 116–125.

SAS-1997-Ramalingam #evaluation #on the

On Sparse Evaluation Representations (GR), pp. 1–15.

DAC-1996-KrauterXDP #image

A Sparse Image Method for BEM Capacitance Extraction (BK, YX, EAD, LTP), pp. 357–362.

SIGMOD-1996-ZouS #online

On-line Reorganization of Sparsely-populated B+trees (CZ, BS), pp. 115–124.

ICPR-1996-HanM

Reconstructing free-form surfaces from sparse data (SH, GGM), pp. 100–104.

ICPR-1996-LiuD #algorithm #performance

Sparse pixel tracking: a fast vectorization algorithm applied to engineering drawings (WL, DD), pp. 808–812.

ICPR-1996-Molina-GamezS #approach #polynomial #recognition

Sparse groups: A polynomial middle-level approach for object recognition (MCMG, JBSV), pp. 518–522.

KDD-1996-Fahner #data mining #interactive #mining

Data Mining with Sparse and Simplified Interaction Selection (GF), pp. 359–362.

SAC-1995-LiangL #algorithm #matrix #representation #scheduling #search-based #using

A sparse matrix representation for production scheduling using genetic algorithms (SJTL, JML), pp. 313–317.

LICS-1995-LynchT #graph #logic #random

The Infinitary Logic of Sparse Random Graphs (JFL, JT), pp. 46–53.

STOC-1994-HalldorssonR #approximate #bound #graph #independence #set

Greed is good: approximating independent sets in sparse and bounded-degree graphs (MMH, JR), pp. 439–448.

ICALP-1993-ChlebusDP #network #performance #reliability

Sparse Networks Supporting Efficient Reliable Broadcasting (BSC, KD, AP), pp. 388–397.

OOPSLA-1993-Driesen #array

Selector Table Indexing & Sparse Arrays (KD), pp. 259–270.

SAC-1993-ZhengLP #network

Sparse Hypercube-Like Interconnection Networks (SQZ, SL, EKP), pp. 694–700.

ICALP-1992-Mansour #approximate #random

Randomized Interpolation and Approximation of Sparse Polynomials (YM), pp. 261–272.

SIGIR-1992-MoffatZ

Parameterised Compression for Sparse Bitmaps (AM, JZ), pp. 274–285.

STOC-1991-CheriyanT #algorithm #parallel

Algorithms for Parallel k-Vertex Connectivity and Sparse Certificates (Extended Abstract) (JC, RT), pp. 391–401.

POPL-1991-ChoiCF #automation #data flow #evaluation #graph

Automatic Construction of Sparse Data Flow Evaluation Graphs (JDC, RC, JF), pp. 55–66.

STOC-1990-BorodinT #decidability #on the #polynomial

On the Decidability of Sparse Univariate Polynomial Interpolation (Preliminary Version) (AB, PT), pp. 535–545.

STOC-1990-OgiwaraW #bound #on the #polynomial #set

On Polynomial Time Bounded Truth-Table Reducibility of NP Sets to Sparse Sets (MO, OW), pp. 457–467.

DAC-1989-SadayappanV #matrix #performance #simulation

Efficient Sparse Matrix Factorization for Circuit Simulation on Vector Supercomputers (PS, VV), pp. 13–18.

STOC-1988-Ben-OrT #algorithm #multi

A Deterministic Algorithm for Sparse Multivariate Polynominal Interpolation (Extended Abstract) (MBO, PT), pp. 301–309.

ICALP-1988-Watanabe #complexity #nondeterminism #on the

On ≤ᴾ₁₋tt-Sparseness and Nondeterministic Complexity Classes (Extended Abstract) (OW0), pp. 697–709.

STOC-1987-GoldbergPS #graph #parallel #symmetry

Parallel Symmetry-Breaking in Sparse Graphs (AVG, SAP, GES), pp. 315–324.

STOC-1983-HartmanisSI #set

Sparse Sets in NP-P: EXPTIME versus NEXPTIME (JH, VS, NI), pp. 382–391.

ICALP-1981-ItaiKR #implementation

A Sparse Table Implementation of Priority Queues (AI, AGK, MR), pp. 417–431.

VLDB-1978-Ashany #analysis #classification #database #matrix #retrieval #scalability

Application of Sparse Matrix Techniques to Search, Retrieval, Classification and Relationship Analysis in Large Data Base Systems — SPARCOM (RA), pp. 499–516.