model compression

More or Less: When and How to Build Convolutional Neural Network Ensembles ICLR 2021 MoViNets: Mobile Video Networks for Efficient Video Recognition CVPR 2021 MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding EMNLP 2021 Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders EACL 2021 Multi-Scale Aligned Distillation for Low-Resolution Detection CVPR 2021 Multi-stage Pre-training over Simplified Multimodal Pre-training Models ACL 2021 NetAdaptV2: Efficient Neural Architecture Search With Fast Super-Network Training and Architecture Optimization CVPR 2021 Network Pruning That Matters: A Case Study on Retraining Variants ICLR 2021 Network Pruning via Performance Maximization CVPR 2021 Network Quantization With Element-Wise Gradient Scaling CVPR 2021 NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application EMNLP 2021 Not all parameters are born equal: Attention is mostly what you need EMNLP 2021 NPAS: A Compiler-Aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration CVPR 2021 On Attention Redundancy: A Comprehensive Study NAACL 2021 Once Quantization-Aware Training: High Performance Extremely Low-Bit Architecture Search ICCV 2021 Online Knowledge Distillation for Efficient Pose Estimation ICCV 2021 Online Multi-Granularity Distillation for GAN Compression ICCV 2021 Only Train Once: A One-Shot Neural Network Training And Pruning Framework NeurIPS 2021 On Self-Distilling Graph Neural Network IJCAI 2021 On the effects of quantisation on model uncertainty in Bayesian neural networks UAI 2021 On the Predictability of Pruning Across Scales ICML 2021 On the Transformer Growth for Progressive BERT Training NAACL 2021 OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization AAAI 2021 Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs CVPR 2021 Optimal Quantization Using Scaled Codebook CVPR 2021