Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: DropNeuron: Simplifying the Structure of Deep Neural Networks
Dropout: a simple way to prevent neural networks from overfitting
Deep Learning in Neural Networks: An Overview
Data-Driven Sparse Structure Selection for Deep Neural Networks
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Improving deep neural networks with multilayer maxout networks
Deep Rewiring: Training very sparse deep networks
Trivializing The Energy Landscape Of Deep Networks.
Adaptive neuron apoptosis for accelerating deep learning on large scale systems
The Power of Depth for Feedforward Neural Networks
Deep Spiking Networks
Avoiding pathologies in very deep networks
Learning hierarchical category structure in deep neural networks
Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error
Understanding the difficulty of training deep feedforward neural networks
Deep, Skinny Neural Networks are not Universal Approximators
Boolean Neural Networks
Neural Networks: Tricks of the Trade
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer
Greedy Layer-Wise Training of Deep Networks
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds
Representation Learning: A Review and New Perspectives
Antialiasing recovery
Experiential learning theory: A dynamic, holistic approach to management learning, education and development
Data Leakage Detection