← Optimization & Theory
Machine Learning › Optimization & Theory ›

Neural Network Optimization

3648 directly classified papers

Papers per year

Papers

Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds NIPS 2018
Visualizing the Loss Landscape of Neural Nets NIPS 2018
L4: Practical loss-based stepsize adaptation for deep learning NIPS 2018
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks NIPS 2018
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data NIPS 2018
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming NIPS 2018
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network NIPS 2018
Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? NIPS 2018
Learning to Teach with Dynamic Loss Functions NIPS 2018
Removing the Feature Correlation Effect of Multiplicative Noise NIPS 2018
Kalman Normalization: Normalizing Internal Representations Across Network Layers NIPS 2018
Can We Gain More from Orthogonality Regularizations in Training Deep Networks? NIPS 2018
Learning Compressed Transforms with Low Displacement Rank NIPS 2018
Training Deep Neural Networks with 8-bit Floating Point Numbers NIPS 2018
Catalyst for Gradient-based Nonconvex Optimization AISTATS 2018
A Generic Approach for Escaping Saddle points AISTATS 2018
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method AISTATS 2018
Semi-tied Units for Efficient Gating in LSTM and Highway Networks INTERSPEECH 2018
Open Problems in Speech Recognition INTERSPEECH 2018
An intriguing failing of convolutional neural networks and the CoordConv solution NIPS 2018
Multilingual Deep Neural Network Training Using Cyclical Learning Rate INTERSPEECH 2018
Total stochastic gradient algorithms and applications in reinforcement learning NIPS 2018
Dendritic cortical microcircuits approximate the backpropagation algorithm NIPS 2018
Gradient Descent for Spiking Neural Networks NIPS 2018
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries NIPS 2018