Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Compensated-DNN: Energy Efficient Low-Precision Deep Neural Networks by Compensating Quantization Errors
deep compression : compressing deep neural network with pruning , trained quantization and huffman coding .
using dataflow to optimize energy efficiency of deep neural network accelerators .
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
Compressing deep neural networks for efficient visual inference
vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design
Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
Towards a Stable Quantized Convolutional Neural Networks: An Embedded Perspective
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
Analytical Guarantees on Numerical Precision of Deep Neural Networks
Model compression via distillation and quantization
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks
A novel channel pruning method for deep neural network compression
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Low precision arithmetic for deep learning.
A Framework for Fast and Efficient Neural Network Compression
SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks
CompactNet: High Accuracy Deep Neural Network Optimized for On-Chip Implementation
A three-tier SDN architecture for DenseNets
Mitochondrial Dysfunction in Parkinson's Disease: Pathogenesis and Neuroprotection
Combating the Small Sample Class Imbalance Problem Using Feature Selection
Very Deep Convolutional Networks for Large-Scale Image Recognition
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization