Model Compression

Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon NeurIPS 2017 meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting ICML 2017 Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee NeurIPS 2017 On Compressing Deep Models by Low Rank and Sparse Decomposition CVPR 2017 ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices ICML 2017 Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations NeurIPS 2017 Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors CVPR 2017 SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization ICML 2017 TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning NeurIPS 2017 Weighted-Entropy-Based Quantization for Deep Neural Networks CVPR 2017 ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning ICML 2017 CNNpack: Packing Convolutional Neural Networks in the Frequency Domain NeurIPS 2016 Convex Regression with Interpretable Sharp Partitions JMLR 2016 Fast ConvNets Using Group-Wise Brain Damage CVPR 2016 Model Compression Applied to Small-Footprint Keyword Spotting Interspeech 2016 On the Efficient Representation and Execution of Deep Acoustic Models Interspeech 2016 Recurrent Dropout without Memory Loss COLING 2016 Is Feature Selection Secure against Training Data Poisoning? ICML 2015 Sparse Convolutional Neural Networks CVPR 2015 Structured Transforms for Small-Footprint Deep Learning NeurIPS 2015 Tensorizing Neural Networks NeurIPS 2015 Do Deep Nets Really Need to be Deep? NeurIPS 2014 Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition NeurIPS 2014 Stochastic Neighbor Compression ICML 2014 Decision Jungles: Compact and Rich Models for Classification NeurIPS 2013