Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: A weight initialization method for improving training speed in feedforward neural network
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
Learning both Weights and Connections for Efficient Neural Networks
The Power of Depth for Feedforward Neural Networks
Introspection: Accelerating Neural Network Training By Learning Weight Evolution
Random feedback weights support learning in deep neural networks
Keeping the neural networks simple by minimizing the description length of the weights
An Optimization Methodology for Neural Network Weights and Architectures
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Deep Neural Network Initialization With Decision Trees
Centered Weight Normalization in Accelerating Training of Deep Neural Networks
A Penalty-Function Approach for Pruning Feedforward Neural Networks
On the Computational Efficiency of Training Neural Networks
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
Simplifying Neural Networks by Soft Weight-Sharing
Feedforward Neural Network Methodology
Understanding the difficulty of training deep feedforward neural networks
Back Propagation is Sensitive to Initial Conditions
Structure mapping in analogy and similarity
Capabilities of threelayered perceptrons
Sexual addiction, sexual compulsivity, sexual impulsivity, or what? Toward a theoretical model
Multi-unit differential auction-barter model for electronic marketplaces
Experience and brain development