← Optimization & Theory
Machine Learning › Optimization & Theory ›

Neural Network Optimization

3648 directly classified papers

Papers per year

Papers

A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification IJCNLP 2017
Highway-LSTM and Recurrent Highway Networks for Speech Recognition INTERSPEECH 2017
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks IJCAI 2017
Differentiable Scheduled Sampling for Credit Assignment ACL 2017
On the Flatness of Loss Surface for Two-layered ReLU Networks ACML 2017
Fast Video Classification via Adaptive Cascading of Deep Models CVPR 2017
Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017
Sharp Minima Can Generalize For Deep Nets ICML 2017
Surprising properties of dropout in deep networks COLT 2017
Approximate Newton Methods and Their Local Convergence ICML 2017
Delta Networks for Optimized Recurrent Network Computation ICML 2017
Learning to Learn without Gradient Descent by Gradient Descent ICML 2017
Implicit Regularization in Matrix Factorization NIPS 2017
Dissipativity Theory for Nesterov’s Accelerated Method ICML 2017
The Shattered Gradients Problem: If resnets are the answer, then what is the question? ICML 2017
How to Escape Saddle Points Efficiently ICML 2017
Dilated Recurrent Neural Networks NIPS 2017
Multiple-Weight Recurrent Neural Networks IJCAI 2017
Mean Field Residual Networks: On the Edge of Chaos NIPS 2017
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models NIPS 2017
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization ICML 2017
Boosting Neural Machine Translation IJCNLP 2017
ASAGA: Asynchronous Parallel SAGA AISTATS 2017
Programming with a Differentiable Forth Interpreter ICML 2017