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Boltzmann machine
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131	Improving neural networks by preventing co-adaptation of feature detectors G. E. Hinton∗ , N. Srivastava, A. Krizhevsky, I. Sutskever and R. R. Salakhutdinov Department of Computer Science, University of Toronto, 6 Kin Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2012-09-17 18:25:11 Neuroscience Feedforward neural network Artificial neural network Backpropagation Boltzmann machine Speech recognition Types of artificial neural networks Recurrent neural network Neural networks Cybernetics Science
132	Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling Andrew Kae∗1 , Kihyuk Sohn∗2 , Honglak Lee2 , Erik Learned-Miller1 1 University of Massachusetts, Amherst, MA, USA, {akae,elm}@cs.umass.edu 2 Uni Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2013-04-21 18:03:25 Bayesian statistics Networks Statistical models Applied mathematics Probability and statistics Conditional random field Boltzmann machine Segmentation Bayesian network Graphical models Statistics Theoretical computer science
133	IMPROVING DEEP NEURAL NETWORKS FOR LVCSR USING RECTIFIED LINEAR UNITS AND DROPOUT George E. Dahl ? Tara N. Sainath† Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2013-08-04 20:58:12 Science Networks Computational neuroscience Network architecture Backpropagation Early stopping Autoencoder Boltzmann machine Bayesian network Neural networks Statistics Machine learning
134	IMPROVED SIMULATED ANNEALING, BOLTZMANN MACHINE, AND ATTRIBUTED GRAPH MATCHING t Lei Xu tt and Erkki Oja Lappeenranta University of Technology, Department of Information Technology BOX 20, 53851 Lappeenranta, Finland A b Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-09-23 15:31:41 Classical cipher Mathematical analysis Mathematics Four-square cipher
135	Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient Tijmen Tieleman [removed] Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-10-01 00:27:20 Learning Statistics Artificial intelligence Computational statistics Boltzmann machine Supervised learning Statistical classification Artificial neural network Pattern recognition Machine learning Neural networks Computational neuroscience
136	REPORTS Fig. 3. Theory, presented as the experiment (see Fig. 1). The SHG source is the magnetic component of the Lorentz force on metal electrons in the SRRs. The setup for measuring the SHG is described Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-10-16 13:22:12 Nonlinear dimensionality reduction Principal component analysis Dimension reduction Boltzmann machine Backpropagation Autoencoder Support vector machine Statistics Neural networks Multivariate statistics
137	A Learning Algorithm for Boltzmann Machine[removed] Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-10-01 23:52:14 Heat transfer Physical quantities Chemistry Hopfield network Boltzmann machine Thermodynamic equilibrium Thermal equilibrium Temperature Ludwig Boltzmann Thermodynamics Neural networks Physics
138	Improved Simulated Annealing, Boltzmann Machine, and Attributed Graph Matching Ran Chen 09/30/2013 Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-10-01 23:41:08 Numerical analysis Science Annealing Ludwig Boltzmann Mathematics Thermodynamic equilibrium Global optimization Quantum annealing Simulated annealing Mathematical optimization Boltzmann machine
139	On the Expressive Power of Deep Architectures Yoshua Bengio and Olivier Delalleau Dept. IRO, Universit´e de Montr´eal. Montr´eal (QC), H3C 3J7, Canada Abstract. Deep architectures are families of functions correspondi Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-09-23 22:24:15 Science Artificial neural network Boltzmann machine Machine learning Supervised learning Unsupervised learning Autoencoder Artificial neuron Algorithm Neural networks Cybernetics Applied mathematics
140	Greedy Layer-Wise Training of Deep Networks Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle Universit´e de Montr´eal Montr´eal, Qu´ebec {bengioy,lamblinp,popovicd,larocheh}@iro.umontreal.ca Add to Reading List Source URL: deeplearning.cs.cmu.edu Language: English - Date: 2013-10-16 13:22:11 Artificial intelligence Computational neuroscience Network architecture Recurrent neural network Feedforward neural network Unsupervised learning Autoencoder Supervised learning Boltzmann machine Neural networks Machine learning Cybernetics

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