Co-occurring keywords · papers mentioning both
Papers
Proximal Backpropagation
ICLR 2018
Regularizing by the Variance of the Activations' Sample-Variances
NeurIPS 2018
Single-Agent Policy Tree Search With Guarantees
NeurIPS 2018
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
ICML 2018
Step Size Matters in Deep Learning
NeurIPS 2018
Training Deep Neural Networks with 8-bit Floating Point Numbers
NeurIPS 2018
Training DNNs with Hybrid Block Floating Point
NeurIPS 2018
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
ICLR 2018
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
NeurIPS 2017
Annealed f-Smoothing as a Mechanism to Speed up Neural Network Training
Interspeech 2017
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
NeurIPS 2017
Global Optimality in Neural Network Training
CVPR 2017
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
NeurIPS 2017
Non-convex Finite-Sum Optimization Via SCSG Methods
NeurIPS 2017
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
NeurIPS 2017
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
NeurIPS 2017
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
EMNLP 2017
Riemannian approach to batch normalization
NeurIPS 2017
Robust Optimization for Non-Convex Objectives
NeurIPS 2017
Sobolev Training for Neural Networks
NeurIPS 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
NeurIPS 2017
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
NeurIPS 2017
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features
COLING 2016
Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach
JMLR 2016
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters
CVPR 2016