Optimization & Theory

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 Query-Key Normalization for Transformers EMNLP 2020 Radial and Directional Posteriors for Bayesian Deep Learning AAAI 2020 Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks OSDI 2020 RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds CVPR 2020 Random Reshuffling is Not Always Better NeurIPS 2020 Rank and run-time aware compression of NLP Applications EMNLP 2020 Rational neural networks NeurIPS 2020 Real-Time Panoptic Segmentation From Dense Detections CVPR 2020 Reborn Filters: Pruning Convolutional Neural Networks with Limited Data AAAI 2020 Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate NeurIPS 2020 Reducing Transformer Depth on Demand with Structured Dropout ICLR 2020 Reducing Underflow in Mixed Precision Training by Gradient Scaling IJCAI 2020 Reformer: The Efficient Transformer ICLR 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks AAAI 2020 Region Normalization for Image Inpainting AAAI 2020 Regularization on Spatio-Temporally Smoothed Feature for Action Recognition CVPR 2020 Regularized linear autoencoders recover the principal components, eventually NeurIPS 2020 Regularize, Expand and Compress: NonExpansive Continual Learning WACV 2020 Regularizing CNN Transfer Learning With Randomised Regression CVPR 2020 Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020 Reinforcing Neural Network Stability with Attractor Dynamics AAAI 2020 Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks AAAI 2020 Rescore in a Flash: Compact, Cache Efficient Hashing Data Structures for n-Gram Language Models Interspeech 2020