Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks
Improved Regularization of Convolutional Neural Networks with Cutout
Feature Contraction: New ConvNet Regularization in Image Classification
When is a Convolutional Filter Easy To Learn?
Learning Deep Networks from Noisy Labels with Dropout Regularization
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
Accelerating convolutional neural networks by group-wise 2D-filter pruning
towards compact convnets via structure - sparsity regularized filter pruning .
Dither is Better than Dropout for Regularising Deep Neural Networks
Convolutional neural networks with low-rank regularization
DisturbLabel: Regularizing CNN on the Loss Layer
The Power of Sparsity in Convolutional Neural Networks
pruning filters for efficient convnets .
Depth Dropout: Efficient Training of Residual Convolutional Neural Networks
Every Filter Extracts A Specific Texture In Convolutional Neural Networks
Recursive autoconvolution for unsupervised learning of convolutional neural networks
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
A Practical Convolutional Neural Network as Loop Filter for Intra Frame
Using Filter Banks in Convolutional Neural Networks for Texture Classification
Small-signal analysis of closed-loop PWM boost converter in CCM with complex impedance load
Secure Virtual Machine Execution under an Untrusted Management OS
An Analysis of Deep Neural Network Models for Practical Applications
Accelerated cone beam CT reconstruction based on OpenCL
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness