Modeling Documents with a Deep Boltzmann Machine Nitish Srivastava Ruslan Salakhutdinov Geoffrey Hinton

Source URL: www.cs.toronto.edu

• Artificial neural networks
• Statistics
• Applied mathematics
• Machine learning
• Multinomial logistic regression
• Restricted Boltzmann machine
• Boltzmann machine
• Latent Dirichlet allocation
• Logistic function
• Multimodal learning
• Deep learning

Where do features come from? Geoffrey Hinton Department of Computer Science, University of Toronto 6 King’s College Rd, M5S 3G4, Canada February 18, 2013

Source URL: www.cs.toronto.edu

• Artificial neural networks
• Restricted Boltzmann machine
• Boltzmann machine
• Autoencoder
• Backpropagation
• Feedforward neural network
• Generative model
• Connectionism
• Supervised learning
• Graphical model
• Machine learning
• Hidden Markov model

The Problem of Boltzmann Brains and How Bohmian Mechanics Helps Solving It Roderich Tumulka Department of Mathematics T¨

Source URL: www.math.rutgers.edu

Restricted Boltzmann Machines for Collaborative Filtering Ruslan Salakhutdinov Andriy Mnih

Source URL: www.cs.toronto.edu

International Renewable Energy Congress November 5-7, Sousse Tunisia Application of the lattice Boltzmann method for solving conduction problems with heat flux boundary condition. Raoudha CHAABANE, Faouzi ASKRI, S

Source URL: 2009.irec-conference.com

REGULARIZATION OF CONTEXT-DEPENDENT DEEP NEURAL NETWORKS WITH CONTEXT-INDEPENDENT MULTI-TASK TRAINING Peter Bell and Steve Renals Centre for Speech Technology Research, University of Edinburgh, Edinburgh EH8 9AB, UK ABST

Source URL: www.cstr.ed.ac.uk

• Artificial neural networks
• Machine learning
• Artificial intelligence
• Applied mathematics
• Speech recognition
• Boltzmann machine
• Recurrent neural network
• Multi-task learning
• Deep belief network
• Backpropagation
• Acoustic model
• Language model

A gentle introduction to maximum entropy, log-linear, exponential, logistic, harmonic, Boltzmann, Markov Random Fields, Conditional Random Fields, etc., models Mark Johnson Department of Computing

Source URL: web.science.mq.edu.au

Typicality and Notions of Probability in Physics∗ Sheldon Goldstein Departments of Mathematics, Physics, and Philosophy – Hill Center Rutgers, The State University of New Jersey 110 Frelinghuysen Road

Source URL: math.rutgers.edu

• Statistical mechanics
• Dynamical systems
• Quantum mechanics
• De BroglieBohm theory
• Quantum measurement
• Distribution function
• Phase space
• Laws of science
• Canonical ensemble
• Ludwig Boltzmann
• Position operator
• MaxwellBoltzmann distribution

The approach to equilibrium in a macroscopic quantum system for a typical nonequilibrium subspace Sheldon Goldstein1 , Takashi Hara2 , and Hal Tasaki3 Abstract We study the problem of the approach to equilibrium in a mac

Source URL: math.rutgers.edu

• Thermodynamics
• Non-equilibrium thermodynamics
• Statistical mechanics
• Quantum mechanics
• Thermodynamic equilibrium
• Eigenstate thermalization hypothesis
• Ludwig Boltzmann
• Distribution
• Entropy
• Thermodynamic system

Real-time Hebbian Learning from Autoencoder Features for Control Tasks To appear in: Proc. of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14). Cambridge, MA: MIT Press

Source URL: eplex.cs.ucf.edu

• Artificial neural networks
• Neuroscience
• Applied mathematics
• Cybernetics
• Autoencoder
• Hebbian theory
• Feature learning
• Deep learning
• Restricted Boltzmann machine
• Unsupervised learning
• Biological neural network
• Backpropagation