Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper
Query: Efficient batchwise dropout training using submatrices
Dropout: a simple way to prevent neural networks from overfitting
Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling.
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Asynchronous stochastic gradient descent for DNN training
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures
Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent
SGDR: Stochastic Gradient Descent with Restarts
Forward-Backward Selection with Early Dropping
Deep Rewiring: Training very sparse deep networks
Accelerator-Aware Pruning for Convolutional Neural Networks
Dropout as a Low-Rank Regularizer for Matrix Factorization
ShakeDrop Regularization for Deep Residual Learning
Dropout as a Bayesian Approximation : Insights and Applications
Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train
Training Faster by Separating Modes of Variation in Batch-normalized Models
Structured Probabilistic Pruning for Convolutional Neural Network Acceleration
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression
A Fast Learning Algorithm for Deep Belief Nets
A Fast Variational Approach for Learning Markov Random Field Language Models
Pros and cons of apomorphine and L-dopa continuous infusion in advanced Parkinson's disease.
Structural Antitumoral Activity Relationships of Synthetic Chalcones