Model Compression

Online Knowledge Distillation via Collaborative Learning CVPR 2020 On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning AAAI 2020 On the Sparsity of Neural Machine Translation Models EMNLP 2020 On the weak link between importance and prunability of attention heads EMNLP 2020 Operation-Aware Soft Channel Pruning using Differentiable Masks ICML 2020 Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems IJCAI 2020 Overview of the SustaiNLP 2020 Shared Task EMNLP 2020 Partial Weight Adaptation for Robust DNN Inference CVPR 2020 PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices AAAI 2020 Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks AAAI 2020 Pruning Filter in Filter NeurIPS 2020 Pruning from Scratch AAAI 2020 Pruning neural networks without any data by iteratively conserving synaptic flow NeurIPS 2020 Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior EMNLP 2020 Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT AAAI 2020 Quantization Aware Training with Absolute-Cosine Regularization for Automatic Speech Recognition Interspeech 2020 Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020 Radial and Directional Posteriors for Bayesian Deep Learning AAAI 2020 Rank and run-time aware compression of NLP Applications EMNLP 2020 Reborn Filters: Pruning Convolutional Neural Networks with Limited Data AAAI 2020 Reducing Transformer Depth on Demand with Structured Dropout ICLR 2020 Reducing Underflow in Mixed Precision Training by Gradient Scaling IJCAI 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks AAAI 2020 Regularize, Expand and Compress: NonExpansive Continual Learning WACV 2020 Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020