Stem boltzmann$ (all stems)

26 papers:

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.

ECIR-2015-YuZHSW #documentation #information retrieval

Document Boltzmann Machines for Information Retrieval (QY, PZ, YH, DS, JW), pp. 666–671.

ICML-2015-LeeY #category theory #predict #strict

Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (TL, SY), pp. 2483–2492.

KDD-2015-XieDX #documentation #modelling #strict

Diversifying Restricted Boltzmann Machine for Document Modeling (PX, YD, EPX), pp. 1315–1324.

ICML-c2-2014-MittelmanKSL #strict

Structured Recurrent Temporal Restricted Boltzmann Machines (RM, BK, SS, HL), pp. 1647–1655.

ICPR-2014-MorenoS #simulation

Volume-Based Fabric Tensors through Lattice-Boltzmann Simulations (RM, ÖS), pp. 3179–3184.

ICPR-2014-TanakaO #novel #strict

A Novel Inference of a Restricted Boltzmann Machine (MT, MO), pp. 1526–1531.

ICPR-2014-YamashitaTYYF #strict

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

ICPR-2014-Yasuda #effectiveness

Effective Mean-Field Inference Method for Nonnegative Boltzmann Machines (MY), pp. 3600–3605.

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.

ICML-c2-2013-SohnZLL #learning

Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines (KS, GZ, CL, HL), pp. 217–225.

ICML-c2-2013-TranPV #learning #multi

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities (TT, DQP, SV), pp. 46–54.

ICML-c3-2013-GeorgievN #collaboration #framework #strict

A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines (KG, PN), pp. 1148–1156.

ICML-2012-DahlAL #strict #word

Training Restricted Boltzmann Machines on Word Observations (GED, RPA, HL), p. 152.

ICPR-2012-YasudaKWT #estimation #strict

Composite likelihood estimation for restricted Boltzmann machines (MY, SK, YW, KT), pp. 2234–2237.

ICML-2011-ChoRI #adaptation #learning #strict

Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (KC, TR, AI), pp. 105–112.

ICML-2010-LongS #approximate #simulation #strict

Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate (PML, RAS), pp. 703–710.

ICML-2010-NairH #linear #strict

Rectified Linear Units Improve Restricted Boltzmann Machines (VN, GEH), pp. 807–814.

ICML-2010-Salakhutdinov #adaptation #learning #using

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

ICML-2009-TaylorH #modelling #strict

Factored conditional restricted Boltzmann Machines for modeling motion style (GWT, GEH), pp. 1025–1032.

ICML-2008-LarochelleB #classification #strict #using

Classification using discriminative restricted Boltzmann machines (HL, YB), pp. 536–543.

ICML-2008-Tieleman #approximate #strict #using

Training restricted Boltzmann machines using approximations to the likelihood gradient (TT), pp. 1064–1071.

RecSys-2008-GunawardanaM #recommendation

Tied boltzmann machines for cold start recommendations (AG, CM), pp. 19–26.

ICML-2007-SalakhutdinovMH #collaboration #strict

Restricted Boltzmann machines for collaborative filtering (RS, AM, GEH), pp. 791–798.

ICALP-2002-DuchonFLS #random

Random Sampling from Boltzmann Principles (PD, PF, GL, GS), pp. 501–513.

HPDC-1992-BetelloRSR #clustering

Lattice Boltzmann Method on a Cluster of IBM RISC System/6000 Workstations (GB, GR, SS, FR), pp. 242–247.