← Optimization & Theory
Machine Learning › Optimization & Theory ›

Neural Network Optimization

3648 directly classified papers

Papers per year

Papers

Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs ICML 2017
Forward and Reverse Gradient-Based Hyperparameter Optimization ICML 2017
Learning Gradient Descent: Better Generalization and Longer Horizons ICML 2017
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis ICML 2017
Learned Optimizers that Scale and Generalize ICML 2017
A Sequence Labeling Convolutional Network and Its Application to Handwritten String Recognition IJCAI 2017
Deep Neural Solver for Math Word Problems EMNLP 2017
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models EMNLP 2017
Depth Separation for Neural Networks COLT 2017
What to Do Next: Modeling User Behaviors by Time-LSTM IJCAI 2017
Unrolling the Shutter: CNN to Correct Motion Distortions CVPR 2017
Recovery Guarantees for One-hidden-layer Neural Networks ICML 2017
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition NIPS 2017
Online Learning with Local Permutations and Delayed Feedback ICML 2017
Global Optimality in Neural Network Training CVPR 2017
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting ICML 2017
Follow the Moving Leader in Deep Learning ICML 2017
Learning ReLUs via Gradient Descent NIPS 2017
Gated End-to-End Memory Networks EACL 2017
Practical Gauss-Newton Optimisation for Deep Learning ICML 2017
Understanding Synthetic Gradients and Decoupled Neural Interfaces ICML 2017
Decoupled Neural Interfaces using Synthetic Gradients ICML 2017
Relative Fisher Information and Natural Gradient for Learning Large Modular Models ICML 2017
Node Pruning Based on Entropy of Weights and Node Activity for Small-Footprint Acoustic Model Based on Deep Neural Networks INTERSPEECH 2017
Random Shifting for CNN: a Solution to Reduce Information Loss in Down-Sampling Layers IJCAI 2017