Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Deep neural networks are robust to weight binarization and other non-linear distortions
Constraint-Aware Deep Neural Network Compression
Binary-Decomposed DCNN for Accelerating Computation and Compressing Model Without Retraining
Improving deep neural network ensembles using reconstruction error
Dither is Better than Dropout for Regularising Deep Neural Networks
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
Learning small-size DNN with output-distribution-based criteria.
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
learning sparse neural networks through l0 regularization .
A Novel Pruning Algorithm for Optimizing Feedforward Neural Network of Classification Problems
Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Auto-balanced Filter Pruning for Efficient Convolutional Neural Networks
Combined Group and Exclusive Sparsity for Deep Neural Networks
Modular Representation of Layered Neural Networks
UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition
Should children drink more water?: The effects of drinking water on cognition in children
A Survey and Future Vision of Data Mining in Educational Field
VMAttack: Deobfuscating Virtualization-Based Packed Binaries
- LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE
Treatment of Acne Vulgaris with 1064 nm Nd:YAG Laser