model compression

Improving Deep Neural Network Sparsity through Decorrelation Regularization IJCAI 2018 Improving DNNs Trained with Non-Native Transcriptions Using Knowledge Distillation and Target Interpolation Interspeech 2018 Interleaved Structured Sparse Convolutional Neural Networks CVPR 2018 KDGAN: Knowledge Distillation with Generative Adversarial Networks NeurIPS 2018 Knowledge Distillation by On-the-Fly Native Ensemble NeurIPS 2018 Learning Compact Neural Networks with Regularization ICML 2018 Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition CVPR 2018 Learning Compressed Transforms with Low Displacement Rank NeurIPS 2018 “Learning-Compression” Algorithms for Neural Net Pruning CVPR 2018 Learning Discrete Weights Using the Local Reparameterization Trick ICLR 2018 Learning Intrinsic Sparse Structures within Long Short-Term Memory ICLR 2018 Learning Sparse Neural Networks through L_0 Regularization ICLR 2018 LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING ICLR 2018 Learning Versatile Filters for Efficient Convolutional Neural Networks NeurIPS 2018 LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation CVPR 2018 Loss-aware Weight Quantization of Deep Networks ICLR 2018 MISSION: Ultra Large-Scale Feature Selection using Count-Sketches ICML 2018 Mobile Video Object Detection With Temporally-Aware Feature Maps CVPR 2018 Model compression via distillation and quantization ICLR 2018 Modulated Convolutional Networks CVPR 2018 Moonshine: Distilling with Cheap Convolutions NeurIPS 2018 Multi-Task Zipping via Layer-wise Neuron Sharing NeurIPS 2018 N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning ICLR 2018 NestedNet: Learning Nested Sparse Structures in Deep Neural Networks CVPR 2018 Network Approximation using Tensor Sketching IJCAI 2018