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Neural Networks

11300 directly classified papers

Papers per year

Papers

Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine NIPS 2010
Inductive Principles for Restricted Boltzmann Machine Learning AISTATS 2010
Neural conditional random fields AISTATS 2010
A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration NIPS 2010
Learning to combine foveal glimpses with a third-order Boltzmann machine NIPS 2010
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010
Learning to localise sounds with spiking neural networks NIPS 2010
Over-complete representations on recurrent neural networks can support persistent percepts NIPS 2010
Phoneme Recognition with Large Hierarchical Reservoirs NIPS 2010
On Herding and the Perceptron Cycling Theorem NIPS 2010
Attractor Dynamics with Synaptic Depression NIPS 2010
Efficient Learning of Deep Boltzmann Machines AISTATS 2010
PyBrain JMLR 2010
Short-term memory in neuronal networks through dynamical compressed sensing NIPS 2010
Code-specific policy gradient rules for spiking neurons NIPS 2009
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation NIPS 2009
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism NIPS 2009
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks NIPS 2009
Measuring Invariances in Deep Networks NIPS 2009
Exploring Strategies for Training Deep Neural Networks JMLR 2009
Replicated Softmax: an Undirected Topic Model NIPS 2009
3D Object Recognition with Deep Belief Nets NIPS 2009
STDP enables spiking neurons to detect hidden causes of their inputs NIPS 2009
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks NIPS 2009
Kernel Methods for Deep Learning NIPS 2009