DIFFERENTIABLE POOLING FOR UNSUPERVISED SPEAKER ADAPTATION Pawel Swietojanski and Steve Renals Centre for Speech Technology Research, University of Edinburgh, Edinburgh EH8 9AB {p.swietojanski, s.renals}@ed.ac.uk ABSTRA

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

• Artificial intelligence
• Automatic identification and data capture
• Artificial neural networks
• Machine learning
• Computational neuroscience
• Learning
• Humancomputer interaction
• Speech recognition
• Convolutional neural network
• Speaker recognition
• Neocognitron
• Unsupervised learning

Unsupervised Learning of Noisy-Or Bayesian Networks Yoni Halpern, David Sontag Department of Computer Science Courant Institute of Mathematical Sciences

Source URL: cs.nyu.edu

Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAIPalo Alto, CA: AAAI Press. Paul A. Szerlip

Source URL: eplex.cs.ucf.edu

• Artificial neural networks
• Artificial intelligence
• Machine learning
• Learning
• Applied mathematics
• Evolutionary algorithms
• Computational neuroscience
• Evolutionary computation
• Neuroevolution
• Deep learning
• HyperNEAT
• Feature learning

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

JMLR: Workshop and Conference Proceedings–15 Workshop on Unsupervised and Transfer Learning Unsupervised and Transfer Learning Challenge: a Deep Learning Approach

Source URL: www-etud.iro.umontreal.ca

Unsupervised Learning of Sparse and Invariant Features Hierarchies Marc’Aurelio Ranzato1 , Y-Lan Boureau1 , Fu Jie Huang1 and Yann LeCun1 1 The Courant Institute of Mathematical Sciences - New York University

Source URL: snowbird.djvuzone.org

An Unsupervised Model for Rule-Based Similarity Learning from Textual Data? Andrzej Janusz1 , Dominik Ślęzak1,2 1 Faculty of Mathematics, Informatics and Mechanics, The University of Warsaw

Source URL: csp2011.mimuw.edu.pl

Efficient, correct, unsupervised learning of context-sensitive languages Alexander Clark Department of Computer Science Royal Holloway, University of London

Source URL: www.cs.rhul.ac.uk

Unsupervised learning Modelling the earliest stages of phonological acquisition Alma de Jonge, Thesis RMA Linguistics, 2013 Thesis advisor: dr. Silke Hamann

Source URL: www.fon.hum.uva.nl

A Topological View of Unsupervised Learning from Noisy Data ∗ P. Niyogi†, S. Smale‡, S. Weinberger§ May 15, 2008 Abstract

Source URL: people.cs.uchicago.edu