Big & Personal: the data and the models behind Netflix recommendations by Xavier Amatriain
CSC321 Lecture 25: More on deep autoencoders & Using stacked, conditional RBMs for modeling sequences Geoffrey Hinton University of Toronto.
Deep learning with multiplicative interactions Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of.
CSC2535 Lecture 4 Boltzmann Machines, Sigmoid Belief Nets and Gibbs sampling Geoffrey Hinton.
Laboratoire de l’Accélérateur Linéaire (LAL) And CERN Marumi Kado Properties, Implications and Prospects Lecture III The Higgs Particle CERN Academic Training.
Introduction to Hidden Markov Models. Set of states: Process moves from one state to another generating a sequence of states : Markov chain property:
Lecture 15 Hidden Markov Models Dr. Jianjun Hu mleg.cse.sc.edu/edu/csce833 CSCE833 Machine Learning University of South Carolina Department of Computer.
Ontologies Reasoning Components Agents Simulations Behavioral Modeling with UML2 Superstructure Jacques Robin.
From Module Breakdown to Interface Specifications Completing the architectural design of Map Schematizer.
Deciding Under Probabilistic Uncertainty Russell and Norvig: Sect. 17.1-3,Chap. 17 CS121 – Winter 2003.
Csc2535 2013 Lecture 8 Modeling image covariance structure Geoffrey Hinton.
Presenters: Sael Lee, Rongjing Xiang, Suleyman Cetintas, Youhan Fang Department of Computer Science, Purdue University Major reference paper: Hinton, G.
