Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper.
Query: AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference
deep compression : compressing deep neural network with pruning , trained quantization and huffman coding .
BranchyNet: Fast inference via early exiting from deep neural networks
Quantized deep neural networks for energy efficient hardware-based inference
Log-DenseNet: How to Sparsify a DenseNet
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit
A simple procedure for pruning back-propagation trained neural networks
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
Pruned Neural Networks for Regression
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs.
Accelerating Deep Convolutional Networks using low-precision and sparsity
Pruning neural networks: is it time to nip it in the bud?
DeepThin: A Self-Compressing Library for Deep Neural Networks
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM
Binary-Decomposed DCNN for Accelerating Computation and Compressing Model Without Retraining
Model compression via distillation and quantization
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices.
Depth Dropout: Efficient Training of Residual Convolutional Neural Networks
Condition monitoring of a complex hydraulic system using multivariate statistics
A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability
Combined Group and Exclusive Sparsity for Deep Neural Networks
Improving zero-shot learning by mitigating the hubness problem
On Text Preprocessing for Opinion Mining Outside of Laboratory Environments