← Architectures
Deep Learning › Architectures ›

Neural Networks

11300 directly classified papers

Papers per year

Papers

Analog readout for optical reservoir computers NIPS 2012
A Better Way to Pretrain Deep Boltzmann Machines NIPS 2012
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction NIPS 2012
Learning with Recursive Perceptual Representations NIPS 2012
Cocktail Party Processing via Structured Prediction NIPS 2012
Cardinality Restricted Boltzmann Machines NIPS 2012
Spiking and saturating dendrites differentially expand single neuron computation capacity NIPS 2012
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection NIPS 2012
Discriminative Learning of Sum-Product Networks NIPS 2012
Compressive neural representation of sparse, high-dimensional probabilities NIPS 2012
Learning to Align from Scratch NIPS 2012
Delay Compensation with Dynamical Synapses NIPS 2012
Deep Learning Made Easier by Linear Transformations in Perceptrons AISTATS 2012
Krylov Subspace Descent for Deep Learning AISTATS 2012
Deep Learners Benefit More from Out-of-Distribution Examples AISTATS 2011
Learning Class-relevant Features and Class-irrelevant Features via a Hybrid third-order RBM AISTATS 2011
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models NIPS 2011
Active dendrites: adaptation to spike-based communication NIPS 2011
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials NIPS 2011
Algorithms for Hyper-Parameter Optimization NIPS 2011
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network NIPS 2011
Shallow vs. Deep Sum-Product Networks NIPS 2011
Sequence learning with hidden units in spiking neural networks NIPS 2011
Learning to Learn with Compound HD Models NIPS 2011
Selecting Receptive Fields in Deep Networks NIPS 2011