Stem convolut$ (all stems)

62 papers:

DAC-2015-CavigelliMB #embedded #network #realtime

Accelerating real-time embedded scene labeling with convolutional networks (LC, MM, LB), p. 6.

DATE-2015-0001B #clustering #energy #manycore #performance

A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters (FC, LB), pp. 683–688.

ECIR-2015-HuynhHR #analysis #learning #sentiment #strict

Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis (TH, YH, SMR), pp. 447–452.

ICML-2015-ClarkS #game studies #network

Training Deep Convolutional Neural Networks to Play Go (CC, AJS), pp. 1766–1774.

ICML-2015-HongYKH #learning #network #online

Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.

SIGIR-2015-SeverynM #learning #network #rank

Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks (AS, AM), pp. 373–382.

SIGIR-2015-SeverynM15a #analysis #network #sentiment #twitter

Twitter Sentiment Analysis with Deep Convolutional Neural Networks (AS, AM), pp. 959–962.

CASE-2014-LinHC #gesture #network #recognition #using

Human hand gesture recognition using a convolution neural network (HIL, MHH, WKC), pp. 1038–1043.

CIKM-2014-ShenHGDM #information retrieval #semantics

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval (YS, XH, JG, LD, GM), pp. 101–110.

ICML-c1-2014-DonahueJVHZTD #named #recognition #visual notation

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (JD, YJ, OV, JH, NZ, ET, TD), pp. 647–655.

ICML-c1-2014-PinheiroC #network

Recurrent Convolutional Neural Networks for Scene Labeling (PHOP, RC), pp. 82–90.

ICML-c1-2014-ZhouT #generative #network #predict #probability

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.

ICPR-2014-DongPHLDJ #classification #network #using

Vehicle Type Classification Using Unsupervised Convolutional Neural Network (ZD, MP, YH, TL, YD, YJ), pp. 172–177.

ICPR-2014-HafemannOC #network #recognition #using

Forest Species Recognition Using Deep Convolutional Neural Networks (LGH, LSO, PRC), pp. 1103–1107.

ICPR-2014-KangKYLD #classification #documentation #image #network

Convolutional Neural Networks for Document Image Classification (LK, JK, PY, YL, DSD), pp. 3168–3172.

ICPR-2014-NieKZ #learning #recognition #using

Periocular Recognition Using Unsupervised Convolutional RBM Feature Learning (LN, AK, SZ), pp. 399–404.

ICPR-2014-RenYZH #classification #image #learning #nearest neighbour

Learning Convolutional Nonlinear Features for K Nearest Neighbor Image Classification (WR, YY, JZ, KH), pp. 4358–4363.

ICPR-2014-SuLTLT #recognition #using

Character Recognition in Natural Scenes Using Convolutional Co-occurrence HOG (BS, SL, ST, JHL, CLT), pp. 2926–2931.

ICPR-2014-WuHYWT #image #network #segmentation

Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation (ZW, YH, YY, LW, TT), pp. 1538–1543.

SAC-2014-MoonKSP #image #novel #scalability

A novel double linear-cubic convolution interpolation for digital image scaling (HMM, KRK, JS, SBP), pp. 1733–1734.

ICDAR-2013-BlucheNK #feature model #network #recognition #word

Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition (TB, HN, CK), pp. 285–289.

ICDAR-2013-CecottiV13a #multi #recognition

A Radial Neural Convolutional Layer for Multi-oriented Character Recognition (HC, SV), pp. 668–672.

ICDAR-2013-ZhuZ #detection #image #recognition #using

Label Detection and Recognition for USPTO Images Using Convolutional K-Means Feature Quantization and Ada-Boost (SZ, RZ), pp. 633–637.

ICML-c1-2013-KarbasiSS #learning

Iterative Learning and Denoising in Convolutional Neural Associative Memories (AK, AHS, AS), pp. 445–453.

ICPR-2012-LinnerS #comparison #normalisation #quality #using

Comparison of restoration quality on square and hexagonal grids using normalized convolution (EL, RS), pp. 3046–3049.

ICPR-2012-SermanetCL #classification #network

Convolutional neural networks applied to house numbers digit classification (PS, SC, YL), pp. 3288–3291.

ICPR-2012-WangWCN #network #recognition

End-to-end text recognition with convolutional neural networks (TW, DJW, AC, AYN), pp. 3304–3308.

ICPR-2012-WuFH0N #network #recognition

Cascaded heterogeneous convolutional neural networks for handwritten digit recognition (CW, WF, YH, JS, SN), pp. 657–660.

ICPR-2012-ZhangW #image #segmentation

An image fusion method based on region segmentation and Cauchy convolution (YQZ, XJW), pp. 392–395.

KDD-2012-0001LHSE #approach #towards

Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach (FW, NL, JH, JS, SE), pp. 453–461.

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-CiresanMGS #classification #network

Convolutional Neural Network Committees for Handwritten Character Classification (DCC, UM, LMG, JS), pp. 1135–1139.

ICALP-v1-2011-CliffordJ #bound #integer #multi #online

Lower Bounds for Online Integer Multiplication and Convolution in the Cell-Probe Model (RC, MJ), pp. 593–604.

CIKM-2011-CroceMB #dependence #kernel #semantics

Semantic convolution kernels over dependency trees: smoothed partial tree kernel (DC, AM, RB), pp. 2013–2016.

ICML-2011-ChenPSDC #analysis #learning #process

The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning (BC, GP, GS, DBD, LC), pp. 361–368.

ICML-2010-JiXYY #3d #network #recognition

3D Convolutional Neural Networks for Human Action Recognition (SJ, WX, MY, KY), pp. 495–502.

ICPR-2010-DengSS #detection #network #pattern matching #pattern recognition #recognition

Applying Error-Correcting Output Coding to Enhance Convolutional Neural Network for Target Detection and Pattern Recognition (HD, GS, CYS), pp. 4291–4294.

ICPR-2010-Perez-CarrascoSASL #network #realtime

Spike-Based Convolutional Network for Real-Time Processing (JAPC, CS, BA, TSG, BLB), pp. 3085–3088.

HPDC-2010-DiasBG

CUDA-based triangulations of convolution molecular surfaces (SD, KB, AJPG), pp. 531–540.

ICDAR-2009-DengSS #network #recognition

Error-Correcting Output Coding for the Convolutional Neural Network for Optical Character Recognition (HD, GS, CYS), pp. 581–585.

ICML-2009-LeeGRN #learning #network #scalability

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.

DATE-2008-VogtW #configuration management #set

A Reconfigurable Application Specific Instruction Set Processor for Convolutional and Turbo Decoding in a SDR Environment (TV, NW), pp. 38–43.

IFL-2008-Hinze #proving #theorem

Scans and Convolutions — A Calculational Proof of Moessner’s Theorem (RH), pp. 1–24.

ICML-2008-ShiBY #learning #modelling #using

Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.

ICML-2008-ShinK #kernel

A generalization of Haussler’s convolution kernel: mapping kernel (KS, TK), pp. 944–951.

STOC-2007-BjorklundHKK #fourier #performance #set

Fourier meets möbius: fast subset convolution (AB, TH, PK, MK), pp. 67–74.

TLCA-2007-Vaux #λ-calculus #μ-calculus

Convolution λμ-Calculus (LV), pp. 381–395.

ICPR-v1-2006-KimC #ambiguity #clustering #permutation

ICA-Based Clustering for Resolving Permutation Ambiguity in Frequency-Domain Convolutive Source Separation (MK, SC), pp. 950–954.

ICPR-v3-2006-ChenHWJF #detection #network

The Application of a Convolution Neural Network on Face and License Plate Detection (YNC, CCH, CTW, BSJ, KCF), pp. 552–555.

ICPR-v4-2006-NeuhausB #graph #kernel

A Convolution Edit Kernel for Error-tolerant Graph Matching (MN, HB), pp. 220–223.

ICPR-v4-2006-TiviveB #classification #gender #network

A Shunting Inhibitory Convolutional Neural Network for Gender Classification (FHCT, AB), pp. 421–424.

ICDAR-2005-CecottiB #adaptation #network #recognition

Rejection strategy for Convolutional Neural Network by adaptive topology applied to handwritten digits recognition (HC, AB), pp. 765–769.

ICDAR-2005-LucasC #grid #performance

Fast Convolutional OCR with the Scanning N-Tuple Grid (SML, KTC), pp. 799–805.

DATE-v2-2004-ZhouZLLZC #analysis #using

Steady-State Analysis of Nonlinear Circuits Using Discrete Singular Convolution Method (XZ, DZ, JL, RL, XZ, CC), pp. 1322–1326.

ICPR-v2-2004-FlusserZ #invariant #symmetry

Invariants to Convolution with Circularly Symmetric PSF (JF, BZ), pp. 11–14.

OOPSLA-2004-ZhangJ04a #middleware

Resolving feature convolution in middleware systems (CZ, HAJ), pp. 188–205.

ICDAR-2003-SimardSP #analysis #documentation #network #visual notation

Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis (PYS, DS, JCP), pp. 958–962.

ICPR-v2-2002-Fasel #analysis #network #robust #using

Robust Face Analysis using Convolutional Neural Networks (BF), pp. 40–43.

ICPR-v3-2000-JohanssonKG #detection #normalisation #symmetry #using

Detecting Rotational Symmetries Using Normalized Convolution (BJ, HK, GHG), pp. 3500–3504.

DATE-1999-Sheehan #reduction #using

Projective Convolution: RLC Model-Order Reduction Using the Impulse Response (BNS), p. 669–?.

ICDAR-1999-TangSMRTC #2d #algorithm #composition

Accelerating the 2-D Mallat Decomposition Algorithm with Cyclical Convolution and FNTT (YYT, QS, HM, DBR, YT, ZKC), pp. 87–90.

PLDI-1991-BromleyHMS #compilation #fortran

Fortran at Ten Gigaflops: The Connection Machine Convolution Compiler (MB, SH, TSM, GLSJ), pp. 145–156.