Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: A pruning based method to learn both weights and connections for LSTM
Learning both Weights and Connections for Efficient Neural Networks
Gradient-Based Learning Algorithms for Recurrent Connectionist Networks
Encouraging LSTMs to Anticipate Actions Very Early
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Professor Forcing: A New Algorithm for Training Recurrent Networks
An interpretable LSTM neural network for autoregressive exogenous model
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
Learning From Graph Neighborhoods Using LSTMs
On the Long-Term Memory of Deep Recurrent Networks
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Learning Natural Language Inference with LSTM
Pruned and Structurally Sparse Neural Networks.
Active Long Term Memory Networks
Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling
Less-forgetting Learning in Deep Neural Networks
Structured Deep Neural Network Pruning by Varying Regularization Parameters.
A Penalty-Function Approach for Pruning Feedforward Neural Networks
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Understanding and Controlling Memory in Recurrent Neural Networks
Neural net pruning-why and how
Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model
Content-addressable memory (CAM) circuits and architectures: a tutorial and survey
Identification of Annotations for Circuit Symbols in Electrical Diagrams of Document Images
A LIDA cognitive model tutorial
A Neural Probabilistic Language Model