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Neural Network Optimization

3,649 papers

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Papers

A Neural Parametric Singing Synthesizer INTERSPEECH 2017
Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017
Accelerating Stochastic Composition Optimization JMLR 2017
Probabilistic Line Searches for Stochastic Optimization JMLR 2017
Stochastic Gradient Descent as Approximate Bayesian Inference JMLR 2017
Maximal Sparsity with Deep Networks? NIPS 2016
Optimal Architectures in a Solvable Model of Deep Networks NIPS 2016
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods NIPS 2016
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NIPS 2016
Residual Networks Behave Like Ensembles of Relatively Shallow Networks NIPS 2016
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes NIPS 2016
Learning Structured Sparsity in Deep Neural Networks NIPS 2016
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences NIPS 2016
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations NIPS 2016
A Multi-Batch L-BFGS Method for Machine Learning NIPS 2016
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds NIPS 2016
Memory-Efficient Backpropagation Through Time NIPS 2016
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques NIPS 2016
Barzilai-Borwein Step Size for Stochastic Gradient Descent NIPS 2016
Direct Feedback Alignment Provides Learning in Deep Neural Networks NIPS 2016
Full-Capacity Unitary Recurrent Neural Networks NIPS 2016
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks NIPS 2016
DeepMath - Deep Sequence Models for Premise Selection NIPS 2016
Deep Learning without Poor Local Minima NIPS 2016