Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper
Query: Depth Dropout: Efficient Training of Residual Convolutional Neural Networks
Deep Networks with Stochastic Depth
super - convergence : very fast training of residual networks using large learning rates .
Channel Pruning for Accelerating Very Deep Neural Networks
Variational Bayesian Dropout.
Refining Architectures of Deep Convolutional Neural Networks
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks
Learning Efficient Convolutional Networks through Network Slimming
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Targeted Dropout
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Accelerator-Aware Pruning for Convolutional Neural Networks
Deep Model for Dropout Prediction in MOOCs
Pruning Convolutional Neural Networks for Resource Efficient Inference
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Avoiding Degradation in Deep Feed-Forward Networks by Phasing Out Skip-Connections
Depth Estimation from Single Image Using CNN-Residual Network
A Deep Primal-Dual Network for Guided Depth Super-Resolution
Visualizing and Understanding Stochastic Depth Networks
Scalable Bayesian Optimization Using Deep Neural Networks
Soft Actuators for Small-Scale Robotics
Design of Half Mode Substrate Integrated Waveguide (HMSIW) filter with series Defective Microstrip Structure (DMS) for Ku band operation
effective inter - component communication mapping in android with epicc : an essential step towards holistic security analysis .
Increasing the Action Gap: New Operators for Reinforcement Learning