Learning to imitate stochastic time series in a compositional way by chaos Accepted in Neural Networks Jun Namikawa

First Page		Document Content
Date: 2011-03-23 09:51:20 Markov models Estimation theory Kullback–Leibler divergence Thermodynamics Recurrent neural network Long short term memory Hidden Markov model Maximum likelihood Markov chain Statistics Neural networks Statistical theory		Learning to imitate stochastic time series in a compositional way by chaos Accepted in Neural Networks Jun Namikawa Add to Reading List Source URL: www.tech.plym.ac.uk Download Document from Source Website File Size: 1.001,66 KB Share Document on Facebook

	Recurrent Network Models for Perfect Temporal Integration of Fluctuating Correlated Inputs Hiroshi Okamoto1,2, Tomoki Fukai1,3 1 Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Saitama, Japan DocID: 1usFW - View Document
	The 2nd CHiME Workshop on Machine Listening in Multisource Environments Vancouver, Canada, June 1st, 2013 RECURRENT NEURAL NETWORK FEATURE ENHANCEMENT: THE 2nd CHIME CHALLENGE DocID: 1uhjL - View Document
	LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos Arjun Sharma Xerox Research Centre India DocID: 1ueYL - View Document
	Robust model predictive control using neural networks Krzysztof Patan1 , Piotr Witczak2 Abstract— The paper deals with robust model predictive control designed using recurrent neural network. A dynamic neural network i DocID: 1u4DX - View Document
	Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation Giuseppe Attardi Universit`a di Pisa Largo B. Pontecorvo, Pisa, Italy DocID: 1tlw2 - View Document