Stem deep$ (all stems)

205 papers:

DATE-2015-ZhangZCY #scalability

Exploiting DRAM restore time variations in deep sub-micron scaling (XZ, YZ, BRC, JY), pp. 477–482.

SIGMOD-2015-AlexandrovKKSTK #parallel

Implicit Parallelism through Deep Language Embedding (AA, AK, AK, FS, LT, OK, TH, VM), pp. 47–61.

VLDB-2015-ShinWWSZR #incremental #knowledge base #using

Incremental Knowledge Base Construction Using DeepDive (JS, SW, FW, CDS, CZ, CR), pp. 1310–1321.

ICSME-2015-CorleyDK #feature model #learning

Exploring the use of deep learning for feature location (CSC, KD, NAK), pp. 556–560.

MSR-2015-WhiteVVP #learning #repository #towards

Toward Deep Learning Software Repositories (MW, CV, MLV, DP), pp. 334–345.

CHI-2015-MentisSPFS #programming

Being Seen: Co-Interpreting Parkinson’s Patient’s Movement Ability in Deep Brain Stimulation Programming (HMM, RS, SP, PF, LS), pp. 511–520.

ECIR-2015-BansalBV #analysis #semantics #towards

Towards Deep Semantic Analysis of Hashtags (PB, RB, VV), pp. 453–464.

ICML-2015-AnBB #how #linear #network #question

How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (SA, FB, MB), pp. 514–523.

ICML-2015-ChenSYU #learning #modelling

Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.

ICML-2015-ClarkS #game studies #network

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

ICML-2015-GanCHCC #analysis #modelling #scalability #topic

Scalable Deep Poisson Factor Analysis for Topic Modeling (ZG, CC, RH, DEC, LC), pp. 1823–1832.

ICML-2015-GuptaAGN #learning #precise

Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.

ICML-2015-IoffeS #network #normalisation

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

ICML-2015-Kandemir #learning #process #symmetry

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

ICML-2015-LongC0J #adaptation #learning #network

Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.

ICML-2015-SnoekRSKSSPPA #network #optimisation #scalability #using

Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.

ICML-2015-Sohl-DicksteinW #learning #using

Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.

ICML-2015-WangALB #learning #multi #on the #representation

On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.

ICML-2015-XuRYLJ

Deep Edge-Aware Filters (LX, JR, QY, RL, JJ), pp. 1669–1678.

KDD-2015-ChangHTQAH #architecture #network

Heterogeneous Network Embedding via Deep Architectures (SC, WH, JT, GJQ, CCA, TSH), pp. 119–128.

KDD-2015-CheKLBL

Deep Computational Phenotyping (ZC, DCK, WL, MTB, YL), pp. 507–516.

KDD-2015-GroverKH #hybrid

A Deep Hybrid Model for Weather Forecasting (AG, AK, EH), pp. 379–386.

KDD-2015-KotziasDFS #using

From Group to Individual Labels Using Deep Features (DK, MD, NdF, PS), pp. 597–606.

KDD-2015-VeeriahDQ #architecture #learning #predict

Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (VV, RD, GJQ), pp. 1205–1214.

KDD-2015-WangWY #collaboration #learning #recommendation

Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.

KDD-2015-YanardagV #graph #kernel

Deep Graph Kernels (PY, SVNV), pp. 1365–1374.

KDD-2015-YanRHC #distributed #learning #modelling #optimisation #performance #scalability

Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems (FY, OR, YH, TMC), pp. 1355–1364.

KDD-2015-ZhangLZSKYJ #analysis #biology #image #learning #modelling #multi

Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis (WZ, RL, TZ, QS, SK, JY, SJ), pp. 1475–1484.

SIGIR-2015-Makhani #personalisation

Structure, Personalization, Scale: A Deep Dive into LinkedIn Search (AM), p. 1081.

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.

OOPSLA-2015-LeSS #compilation #debugging #probability

Finding deep compiler bugs via guided stochastic program mutation (VL, CS, ZS), pp. 386–399.

POPL-2015-GuKRSWWZG #abstraction #specification

Deep Specifications and Certified Abstraction Layers (RG, JK, TR, ZS, X(W, SCW, HZ, YG), pp. 595–608.

SAC-2015-ReadPB #data type #learning

Deep learning in partially-labeled data streams (JR, FPC, AB), pp. 954–959.

ICSE-v2-2015-White #re-engineering

Deep Representations for Software Engineering (MW), pp. 781–783.

CGO-2015-McAfeeO #framework #generative #learning #multi #named

EMEURO: a framework for generating multi-purpose accelerators via deep learning (LCM, KO), pp. 125–135.

DATE-2014-HanKNV #learning

A deep learning methodology to proliferate golden signoff timing (SSH, ABK, SN, ASV), pp. 1–6.

VLDB-2014-ZouJLGWX #framework #learning #named

Mariana: Tencent Deep Learning Platform and its Applications (YZ, XJ, YL, ZG, EW, BX), pp. 1772–1777.

ICFP-2014-GibbonsW #domain-specific language #functional

Folding domain-specific languages: deep and shallow embeddings (functional Pearl) (JG, NW), pp. 339–347.

CSCW-2014-BhattacharyaGKMZGG #microblog #scalability #topic #twitter

Deep Twitter diving: exploring topical groups in microblogs at scale (PB, SG, JK, MM, MBZ, NG, KPG), pp. 197–210.

CAiSE-2014-NeumayrJSS #concept #implementation

Dual Deep Instantiation and Its ConceptBase Implementation (BN, MAJ, MS, CGS), pp. 503–517.

EDOC-2014-GrogerSM #integration

The Deep Data Warehouse: Link-Based Integration and Enrichment of Warehouse Data and Unstructured Content (CG, HS, BM), pp. 210–217.

ECIR-2014-QiDCW #information management #learning

Deep Learning for Character-Based Information Extraction (YQ, SGD, RC, JW), pp. 668–674.

ICML-c1-2014-AroraBGM #bound #learning

Provable Bounds for Learning Some Deep Representations (SA, AB, RG, TM), pp. 584–592.

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-UriaML

A Deep and Tractable Density Estimator (BU, IM, HL), pp. 467–475.

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.

ICML-c2-2014-BengioLAY #generative #network #probability

Deep Generative Stochastic Networks Trainable by Backprop (YB, EL, GA, JY), pp. 226–234.

ICML-c2-2014-CortesMS

Deep Boosting (CC, MM, US), pp. 1179–1187.

ICML-c2-2014-GregorDMBW #network

Deep AutoRegressive Networks (KG, ID, AM, CB, DW), pp. 1242–1250.

ICML-c2-2014-PandeyD #learning #network

Learning by Stretching Deep Networks (GP, AD), pp. 1719–1727.

ICML-c2-2014-RezendeMW #approximate #generative #modelling #probability

Stochastic Backpropagation and Approximate Inference in Deep Generative Models (DJR, SM, DW), pp. 1278–1286.

ICML-c2-2014-TrigeorgisBZS #learning

A Deep Semi-NMF Model for Learning Hidden Representations (GT, KB, SZ, BWS), pp. 1692–1700.

ICPR-2014-DuHZWD #case study #classification #design #network #online #recognition #using

A Study of Designing Compact Classifiers Using Deep Neural Networks for Online Handwritten Chinese Character Recognition (JD, JSH, BZ, SW, LRD), pp. 2950–2955.

ICPR-2014-HafemannOC #network #recognition #using

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

ICPR-2014-HuangHWW #clustering #network

Deep Embedding Network for Clustering (PH, YH, WW, LW), pp. 1532–1537.

ICPR-2014-HuangW0T #framework #network

A General Nonlinear Embedding Framework Based on Deep Neural Network (YH, WW, LW, TT), pp. 732–737.

ICPR-2014-JhuoL #detection #learning #multi #video

Video Event Detection via Multi-modality Deep Learning (IHJ, DTL), pp. 666–671.

ICPR-2014-WuJ #detection #learning

Learning the Deep Features for Eye Detection in Uncontrolled Conditions (YW, QJ), pp. 455–459.

ICPR-2014-YiLLL #identification #learning #metric

Deep Metric Learning for Person Re-identification (DY, ZL, SL, SZL), pp. 34–39.

ICPR-2014-YinYPH #case study #classification #learning

Shallow Classification or Deep Learning: An Experimental Study (XCY, CY, WYP, HWH), pp. 1904–1909.

ICPR-2014-ZhangLYQWTZ #detection #statistics

Sufficient Statistics Feature Mapping over Deep Boltzmann Machine for Detection (CZ, XL, JY, SQ, YW, CT, YZ), pp. 827–832.

KDD-2014-Bengio #learning #scalability

Scaling up deep learning (YB), p. 1966.

KDD-2014-PerozziAS #learning #named #online #social

DeepWalk: online learning of social representations (BP, RAR, SS), pp. 701–710.

KDD-2014-Salakhutdinov #learning

Deep learning (RS), p. 1973.

KDD-2014-ZhangTMF #learning #network

Supervised deep learning with auxiliary networks (JZ, GT, YM, WF), pp. 353–361.

MLDM-2014-BugaychenkoZ #diagrams #learning #multi #pattern matching #pattern recognition #performance #recognition #using

Fast Pattern Recognition and Deep Learning Using Multi-Rooted Binary Decision Diagrams (DB, DZ), pp. 73–77.

ECOOP-2014-ScherrC #staging

Implicit Staging of EDSL Expressions: A Bridge between Shallow and Deep Embedding (MS, SC), pp. 385–410.

GPCE-2014-JovanovicSSNKO #domain-specific language #named

Yin-yang: concealing the deep embedding of DSLs (VJ, AS, SS, VN, CK, MO), pp. 73–82.

OSDI-2014-ChilimbiSAK #learning #performance #scalability

Project Adam: Building an Efficient and Scalable Deep Learning Training System (TMC, YS, JA, KK), pp. 571–582.

OSDI-2014-LeesatapornwongsaHJLG #debugging #model checking #named #performance #semantics

SAMC: Semantic-Aware Model Checking for Fast Discovery of Deep Bugs in Cloud Systems (TL, MH, PJ, JFL, HSG), pp. 399–414.

LICS-CSL-2014-Das #on the

On the pigeonhole and related principles in deep inference and monotone systems (AD), p. 10.

SIGMOD-2013-ZhangGBFRP #named #statistics #using

GeoDeepDive: statistical inference using familiar data-processing languages (CZ, VG, JB, TF, CR, SP), pp. 993–996.

CHI-2013-ShrinivasanJSCHDM #design

Deep conservation in urban India and its implications for the design of conservation technologies (YBS, MJ, DPS, AC, EMH, TD, JM), pp. 1969–1978.

HCI-IMT-2013-BoyP

A Situation Awareness Assistant for Human Deep Space Exploration (GAB, DP), pp. 629–636.

ICML-c1-2013-BengioMDR

Better Mixing via Deep Representations (YB, GM, YD, SR), pp. 552–560.

ICML-c3-2013-AndrewABL #analysis #canonical #correlation

Deep Canonical Correlation Analysis (GA, RA, JAB, KL), pp. 1247–1255.

ICML-c3-2013-CoatesHWWCN #learning #off the shelf

Deep learning with COTS HPC systems (AC, BH, TW, DJW, BCC, AYN), pp. 1337–1345.

ICML-c3-2013-JoseGAV #kernel #learning #performance #predict

Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (CJ, PG, PA, MV), pp. 486–494.

ICML-c3-2013-SutskeverMDH #learning #on the

On the importance of initialization and momentum in deep learning (IS, JM, GED, GEH), pp. 1139–1147.

KDD-2013-GeGLZ #estimation #learning #multi

Multi-source deep learning for information trustworthiness estimation (LG, JG, XL, AZ), pp. 766–774.

KDD-2013-Howard #learning

The business impact of deep learning (JH), p. 1135.

VLDB-2012-RoyDMSW #analysis

Massive Genomic Data Processing and Deep Analysis (AR, YD, EM, YS, BLW), pp. 1906–1909.

VLDB-2013-LiDLMS12 #problem #question #web

Truth Finding on the Deep Web: Is the Problem Solved? (XL, XLD, KL, WM, DS), pp. 97–108.

ITiCSE-2012-Retik #education #visual notation

Visual search with deep zoom to explore curriculum resources interactively (AR), p. 405.

SAS-2012-Distefano

A Voyage to the Deep-Heap (DD), p. 3.

CIKM-2012-LiuQCH #logic

Discovering logical knowledge for deep question answering (ZL, XQ, LC, XH), pp. 1920–1924.

ICML-2012-TangSH

Deep Mixtures of Factor Analysers (YT, RS, GEH), p. 147.

ICML-2012-TangSH12a #network

Deep Lambertian Networks (YT, RS, GEH), p. 184.

ICML-2012-YuSL #network #speech #using

Conversational Speech Transcription Using Context-Dependent Deep Neural Networks (DY, FS, GL), p. 1.

ICPR-2012-PorwalZG #network #recognition #using

Handwritten Arabic text recognition using Deep Belief Networks (UP, YZ, VG), pp. 302–305.

KDD-2012-LiuA #clustering #data flow #web

Stratified k-means clustering over a deep web data source (TL, GA), pp. 1113–1121.

ASPLOS-2012-MeisnerW #architecture #named

DreamWeaver: architectural support for deep sleep (DM, TFW), pp. 313–324.

DATE-2011-LiMY #independence

Redressing timing issues for speed-independent circuits in deep submicron age (YL, TSTM, AY), pp. 1376–1381.

VLDB-2011-0004D #repository #web

Exploration of Deep Web Repositories (NZ, GD), pp. 1506–1507.

SEFM-2011-JacquelBDD #automation #proving #theorem proving #using #verification

Verifying B Proof Rules Using Deep Embedding and Automated Theorem Proving (MJ, KB, DD, CD), pp. 253–268.

ICFP-2011-Mitchell #ecosystem #functional #modelling #programming

Functional programming through deep time: modeling the first complex ecosystems on earth (EGM), pp. 28–31.

AGTIVE-2011-RossiniLGRL #graph transformation #metamodelling #semantics

A Graph Transformation-Based Semantics for Deep Metamodelling (AR, JdL, EG, AR, YL), pp. 19–34.

CHI-2011-ChangL #framework #migration #mobile #using

Deep shot: a framework for migrating tasks across devices using mobile phone cameras (THC, YL), pp. 2163–2172.

CHI-2011-ReederBCRV #usability

More than skin deep: measuring effects of the underlying model on access-control system usability (RWR, LB, LFC, MKR, KV), pp. 2065–2074.

CIKM-2011-LiuLZD #classification #network #sentiment

Sentiment classification via l2-norm deep belief network (TL, ML, SZ, XD), pp. 2489–2492.

CIKM-2011-OroR #approach #learning #named

SILA: a spatial instance learning approach for deep webpages (EO, MR), pp. 2329–2332.

CIKM-2011-WangA #data flow #effectiveness #query #web

Effective stratification for low selectivity queries on deep web data sources (FW, GA), pp. 1455–1464.

ICML-2011-BazzaniFLMT #learning #network #policy #recognition #video

Learning attentional policies for tracking and recognition in video with deep networks (LB, NdF, HL, VM, JAT), pp. 937–944.

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-2011-GlorotBB #adaptation #approach #classification #learning #scalability #sentiment

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (XG, AB, YB), pp. 513–520.

ICML-2011-LeNCLPN #learning #on the #optimisation

On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.

ICML-2011-NgiamCKN #energy #learning #modelling

Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.

ICML-2011-NgiamKKNLN #learning #multimodal

Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.

KDIR-2011-CostantiniFP #analysis #framework #natural language #representation

A Framework for Structured Knowledge Extraction and Representation from Natural Language through Deep Sentence Analysis (SC, NF, AP), pp. 282–287.

SIGIR-2011-SunWY #classification #effectiveness #towards

Towards effective short text deep classification (XS, HW, YY), pp. 1143–1144.

MoDELS-2011-KainzBK #automation #concept #metamodelling #model transformation

Automated Model-to-Metamodel Transformations Based on the Concepts of Deep Instantiation (GK, CB, AK), pp. 17–31.

MoDELS-2011-KainzBK #automation #concept #metamodelling #model transformation

Automated Model-to-Metamodel Transformations Based on the Concepts of Deep Instantiation (GK, CB, AK), pp. 17–31.

ESEC-FSE-2011-CifuentesKLHVBZCTH #fault #scalability #using

Static deep error checking in large system applications using parfait (CC, NK, LL, NH, MV, AB, JZ, AC, DT, CH), pp. 432–435.

TLCA-2011-Roversi #linear #λ-calculus

Linear λ Calculus and Deep Inference (LR), pp. 184–197.

DAC-2010-BanP #layout #modelling #optimisation #robust

Compact modeling and robust layout optimization for contacts in deep sub-wavelength lithography (YB, DZP), pp. 408–411.

DATE-2010-KennedyWLL #string #throughput

Ultra-high throughput string matching for Deep Packet Inspection (AK, XW, ZL, BL), pp. 399–404.

HT-2010-ZhangQHJWHHJ #approach #collaboration #hybrid #identification #web

Collaborative identification and annotation of government deep web resources: a hybrid approach (PZ, YQ, CH, PTJ, JW, WSH, JEH, XJ), pp. 285–286.

VLDB-2010-KabischDYL #integration #web

Deep Web Integration with VisQI (TK, ECD, CTY, UL), pp. 1613–1616.

VLDB-2010-TermehchyW #keyword #named #using #xml

EXTRUCT: Using Deep Structural Information in XML Keyword Search (AT, MW), pp. 1593–1596.

ICML-2010-GrubbB #composition #learning #network

Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.

ICML-2010-Martens #learning #optimisation

Deep learning via Hessian-free optimization (JM), pp. 735–742.

ICML-2010-MinMYBZ

Deep Supervised t-Distributed Embedding (MRM, LvdM, ZY, AJB, ZZ), pp. 791–798.

ICML-2010-Salakhutdinov #adaptation #learning #using

Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.

ICML-2010-TangE #network #recognition #robust #visual notation

Deep networks for robust visual recognition (YT, CE), pp. 1055–1062.

ICPR-2010-CarneiroN #architecture #learning

The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking (GC, JCN), pp. 2065–2068.

ICPR-2010-FaselB #network #realtime #speech

Deep Belief Networks for Real-Time Extraction of Tongue Contours from Ultrasound During Speech (IF, JB), pp. 1493–1496.

ICPR-2010-ZhouCW #classification #network #quantum

Deep Quantum Networks for Classification (SZ, QC, XW), pp. 2885–2888.

TOOLS-EUROPE-2010-LaraG #metamodelling

Deep Meta-modelling with MetaDepth (JdL, EG), pp. 1–20.

SAC-2010-OhCM #classification #information management

Combining global and local information for enhanced deep classification (HSO, YC, SHM), pp. 1760–1767.

SAC-2010-ShestakovS #clustering #web

Host-IP clustering technique for deep web characterization (DS, TS), pp. 874–875.

VLDB-2009-Rajaraman #named #topic #using #web

Kosmix: High-Performance Topic Exploration using the Deep Web (AR), pp. 1524–1529.

ICSM-2009-StroblBGK #database #legacy #re-engineering #reverse engineering

Digging deep: Software reengineering supported by database reverse engineering of a system with 30+ years of legacy (SS, MB, TG, WK), pp. 407–410.

ICML-2009-DavisD #higher-order #logic #markov

Deep transfer via second-order Markov logic (JD, PMD), pp. 217–224.

ICML-2009-LeeGRN #learning #network #scalability

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

ICML-2009-MobahiCW #learning #video

Deep learning from temporal coherence in video (HM, RC, JW), pp. 737–744.

ICML-2009-RainaMN #learning #scalability #using

Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.

KEOD-2009-GrozaH #approach #hybrid #metadata #towards

A Hybrid Approach Towards Information Expansion based on Shallow and Deep Metadata (TG, SH), pp. 109–116.

SIGIR-2009-YilmazR #learning #rank

Deep versus shallow judgments in learning to rank (EY, SR), pp. 662–663.

CGO-2009-PereiraB #analysis #pointer

Wave Propagation and Deep Propagation for Pointer Analysis (FMQP, DB), pp. 126–135.

DATE-2008-Schat #clustering #fault #process

Fault Clustering in deep-submicron CMOS Processes (JS), pp. 511–514.

DATE-2008-ZengC #analysis #polynomial #random

Deep Submicron Interconnect Timing Model with Quadratic Random Variable Analysis (JKZ, CPC), pp. 1091–1094.

VLDB-2008-MadhavanKKGRH #web

Google’s Deep Web crawl (JM, DK, LK, VG, AR, AYH), pp. 1241–1252.

MSR-2008-HolmesB #information management

Deep intellisense: a tool for rehydrating evaporated information (RH, AB), pp. 23–26.

AFL-2008-Leupold #how #matter

How to Pop a Deep PDA Matters (PL), pp. 281–291.

CIKM-2008-Lu #data flow #estimation #performance #web

Efficient estimation of the size of text deep web data source (JL), pp. 1485–1486.

ICML-2008-CollobertW #architecture #learning #multi #natural language #network

A unified architecture for natural language processing: deep neural networks with multitask learning (RC, JW), pp. 160–167.

ICML-2008-RanzatoS #documentation #learning #network

Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.

ICML-2008-SalakhutdinovM #analysis #network #on the

On the quantitative analysis of deep belief networks (RS, IM), pp. 872–879.

ICML-2008-WestonRC #learning

Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.

SIGIR-2008-XueXYY #classification #scalability

Deep classification in large-scale text hierarchies (GRX, DX, QY, YY), pp. 619–626.

OOPSLA-2008-TatlockTSJL #refactoring

Deep typechecking and refactoring (ZT, CT, DS, RJ, SL), pp. 37–52.

ICML-2007-LarochelleECBB #architecture #empirical #evaluation #problem

An empirical evaluation of deep architectures on problems with many factors of variation (HL, DE, ACC, JB, YB), pp. 473–480.

SEKE-2007-CordeiroGES #classification #constraints #database #design #version control

A Deep Classification of Temporal Versioned Integrity Constraints for Designing Database Applications (RLFC, RdMG, NE, CSdS), pp. 416–421.

SAC-2007-AnGWC #automation #data flow #semantics #web

Semantic deep web: automatic attribute extraction from the deep web data sources (YJA, JG, YTW, SAC), pp. 1667–1672.

DATE-2006-KaneMS #pipes and filters #verification

Monolithic verification of deep pipelines with collapsed flushing (RK, PM, SKS), pp. 1234–1239.

DATE-2006-NiclassSC #array

A single photon avalanche diode array fabricated in deep-submicron CMOS technology (CN, MS, EC), pp. 81–86.

LDTA-2006-Helin

Combining Deep and Shallow Embeddings (JH), pp. 61–79.

DATE-2005-LiS #performance #simulation

An Efficiently Preconditioned GMRES Method for Fast Parasitic-Sensitive Deep-Submicron VLSI Circuit Simulation (ZL, CJRS), pp. 752–757.

DATE-2005-WangMDCM #analysis #embedded #energy #process #variability

Systematic Analysis of Energy and Delay Impact of Very Deep Submicron Process Variability Effects in Embedded SRAM Modules (HW, MM, WD, FC, KM), pp. 914–919.

VLDB-2005-HeMYW #interface #named #web

WISE-Integrator: A System for Extracting and Integrating Complex Web Search Interfaces of the Deep Web (HH, WM, CTY, ZW), pp. 1314–1317.

DAC-2004-EkpanyapongMWLL #architecture #design

Profile-guided microarchitectural floorplanning for deep submicron processor design (ME, JRM, TW, HHSL, SKL), pp. 634–639.

DATE-DF-2004-PanatoSWJRB #design #multi #pipes and filters

Design of Very Deep Pipelined Multipliers for FPGAs (AP, SVS, FRW, MOJ, RR, SB), pp. 52–57.

DATE-v1-2004-ThepayasuwanD #architecture #layout #synthesis

Layout Conscious Bus Architecture Synthesis for Deep Submicron Systems on Chip (NT, AD), pp. 108–113.

DATE-v1-2004-WongT #configuration management #encoding #power management

Re-Configurable Bus Encoding Scheme for Reducing Power Consumption of the Cross Coupling Capacitance for Deep Sub-Micron Instruction Bus (SKW, CYT), pp. 130–135.

DATE-v2-2004-BernardiniPM

A Tunneling Model for Gate Oxide Failure in Deep Sub-Micron Technology (SB, JMP, PM), pp. 1404–1405.

DATE-v2-2004-MangoCWC #fault #testing

Pattern Selection for Testing of Deep Sub-Micron Timing Defects (MCTC, LCW, KTC), p. 160.

DATE-v2-2004-RosselloS

A Compact Propagation Delay Model for Deep-Submicron CMOS Gates including Crosstalk (JLR, JS), pp. 954–961.

SIGMOD-2004-HeZC #integration #interface #query #web

Knocking the Door to the Deep Web: Integration of Web Query Interfaces (BH, ZZ, KCCC), pp. 913–914.

SIGMOD-2004-WuYDM #approach #clustering #interactive #interface #query #web

An Interactive Clustering-based Approach to Integrating Source Query interfaces on the Deep Web (WW, CTY, AD, WM), pp. 95–106.

VLDB-2004-GraupmannBZZBTW #concept #html #named #web #xml

COMPASS: A Concept-based Web Search Engine for HTML, XML, and Deep Web Data (JG, MB, CZ, PZ, MB, MT, GW), pp. 1313–1316.

CSL-2004-Gianantonio #linear #logic #multi

Structures for Multiplicative Cyclic Linear Logic: Deepness vs Cyclicity (PDG), pp. 130–144.

ECIR-2003-Jones #documentation #retrieval

Document Retrieval: Shallow Data, Deep Theories; Historical Reflections, Potential Directions (KSJ), pp. 1–11.

DAC-2002-HazelhurstWKF #approach #design #hybrid #verification

A hybrid verification approach: getting deep into the design (SH, OW, GK, LF), pp. 111–116.

HPCA-2002-YangPFV #design #energy

Exploiting Choice in Resizable Cache Design to Optimize Deep-Submicron Processor Energy-Delay (SHY, MDP, BF, TNV), pp. 151–161.

DAC-2001-HenkelL #adaptation #design #named #power management

A2BC: Adaptive Address Bus Coding for Low Power Deep Sub-Micron Designs (JH, HL), pp. 744–749.

DATE-2001-BayraktarogluO

Diagnosis for scan-based BIST: reaching deep into the signatures (IB, AO), pp. 102–111.

DATE-2001-Bazargan-SabetI #modelling #tool support #verification

Modeling crosstalk noise for deep submicron verification tools (PBS, FI), pp. 530–534.

SIGIR-2001-AllenL #distributed #query #web

Searching the Deep Web — Distributed Explorit Directed Query Applications (VSA, AL), p. 456.

HPCA-2001-YangPFRV #approach #architecture

An Integrated Circuit/Architecture Approach to Reducing Leakage in Deep-Submicron High-Performance I-Caches (SHY, MDP, BF, KR, TNV), pp. 147–157.

DAC-2000-ChuengDRR #challenge

Test challenges for deep sub-micron technologies (KTC, SD, MR, KR), pp. 142–149.

DAC-2000-LevyBBDGOOSZ #analysis #design #named

ClariNet: a noise analysis tool for deep submicron design (RL, DB, GB, AD, AG, CO, BO, SS, VZ), pp. 233–238.

PADL-2000-Schulte #combinator #concurrent #constraints #programming

Programming Deep Concurrent Constraint Combinators (CS), pp. 215–229.

DAC-1999-BanerjeeMSH #on the

On Thermal Effects in Deep Sub-Micron VLSI Interconnects (KB, AM, ALSV, CH), pp. 885–891.

DAC-1999-CongP #design #estimation

Interconnect Estimation and Dlanning for Deep Submicron Designs (JC, DZP), pp. 507–510.

DAC-1999-JiangC #analysis #performance #power management

Analysis of Performance Impact Caused by Power Supply Noise in Deep Submicron Devices (YMJ, KTC), pp. 760–765.

DAC-1999-KhatriMBOS #layout #novel

A Novel VLSI Layout Fabric for Deep Sub-Micron Applications (SPK, AM, RKB, RHJMO, ALSV), pp. 491–496.

DAC-1999-YimK #design

Reducing Cross-Coupling Among Interconnect Wires in Deep-Submicron Datapath Design (JSY, CMK), pp. 485–490.

DATE-1999-ToulouseBLN #3d #modelling #performance

Efficient 3D Modelling for Extraction of Interconnect Capacitances in Deep Submicron Dense Layouts (AT, DB, CL, PN), pp. 576–580.

DATE-1999-YeCFCNC #design #verification

Chip-Level Verification for Parasitic Coupling Effects in Deep-Submicron Digital Designs (LY, FCC, PF, RC, NN, FC), pp. 658–663.

DATE-1998-Rodriguez-MontanesF #estimation

Estimation of the Defective IDDQ Caused by Shorts in Deep-Submicron CMOS ICs (RRM, JF), pp. 490–494.

DAC-1997-ChenL #analysis #design #power management

Power Supply Noise Analysis Methodology for Deep-Submicron VLSI Chip Design (HHC, DDL), pp. 638–643.

DAC-1997-Man #education #question

Education for the Deep Submicron Age: Business as Usual? (HDM), pp. 307–312.

EDTC-1997-KunduG #analysis

Inductance analysis of on-chip interconnects [deep submicron CMOS] (SK, UG), pp. 252–255.

EDTC-1997-Sachdev #testing

Deep sub-micron IDDQ testing: issues and solutions (MS), pp. 271–278.

KDD-1997-HahnS #information management #natural language

Deep Knowledge Discovery from Natural Language Texts (UH, KS), pp. 175–178.

DAC-1996-SatoKEM #design #optimisation

Post-Layout Optimization for Deep Submicron Design (KS, MK, HE, NM), pp. 740–745.

ICPR-1996-DugelayGA #multi #segmentation

Segmentation of multibeam acoustic imagery in the exploration of the deep sea-bottom (SD, CG, JMA), pp. 437–446.

KDD-1996-RicheldiL #effectiveness #feature model

Performing Effective Feature Selection by Investigating the Deep Structure of the Data (MR, PLL), pp. 379–383.

ICML-1993-GratchCD #learning #network #scheduling

Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.

VLDB-1992-ChenLYY #execution #pipes and filters #using

Using Segmented Right-Deep Trees for the Execution of Pipelined Hash Joins (MSC, MLL, PSY, HCY), pp. 15–26.

SIGMOD-1991-IoannidisK #analysis #optimisation #query

Left-Deep vs. Bushy Trees: An Analysis of Strategy Spaces and its Implications for Query Optimization (YEI, YCK), pp. 168–177.

ML-1991-FisherY #similarity

Combining Evidence of Deep and Surface Similarity (DHF, JPY), pp. 46–50.

HCI-CE-1987-YoonH #fault

A Deep-Reasoning Aid for Deep-Reasoning Fault Diagnosis (WCY, JMH), pp. 297–304.

DAC-1980-KoppelmanM #logic #verification

Verifying deep logic hierarchies with ALEX (GMK, KM), pp. 328–335.