model compression

Overcoming Multi-model Forgetting ICML 2019 Parameter-Efficient Transfer Learning for NLP ICML 2019 Patient Knowledge Distillation for BERT Model Compression EMNLP 2019 Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm ICLR 2019 Play and Prune: Adaptive Filter Pruning for Deep Model Compression IJCAI 2019 Positive-Unlabeled Compression on the Cloud NeurIPS 2019 Post training 4-bit quantization of convolutional networks for rapid-deployment NeurIPS 2019 PRADO: Projection Attention Networks for Document Classification On-Device EMNLP 2019 Private Model Compression via Knowledge Distillation AAAI 2019 Probability Distillation: A Caveat and Alternatives UAI 2019 Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation AAAI 2019 Proximal Mean-Field for Neural Network Quantization ICCV 2019 ProxQuant: Quantized Neural Networks via Proximal Operators ICLR 2019 ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware ICLR 2019 Quantization Networks CVPR 2019 Rate Distortion For Model Compression:From Theory To Practice ICML 2019 RDPD: Rich Data Helps Poor Data via Imitation IJCAI 2019 Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs IJCAI 2019 Recurrent Stacking of Layers for Compact Neural Machine Translation Models AAAI 2019 Regularizing Activation Distribution for Training Binarized Deep Networks CVPR 2019 Relational Knowledge Distillation CVPR 2019 Relaxed Quantization for Discretized Neural Networks ICLR 2019 RePr: Improved Training of Convolutional Filters CVPR 2019 Rethinking the Value of Network Pruning ICLR 2019 RNN Architecture Learning with Sparse Regularization EMNLP 2019