Optimization & Theory

Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors CVPR 2020 DFQF: Data Free Quantization-aware Fine-tuning ACML 2020 diagNNose: A Library for Neural Activation Analysis EMNLP 2020 DIANet: Dense-and-Implicit Attention Network AAAI 2020 Dice Loss for Data-imbalanced NLP Tasks ACL 2020 Directional convergence and alignment in deep learning NeurIPS 2020 Discrete Model Compression With Resource Constraint for Deep Neural Networks CVPR 2020 Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation EMNLP 2020 Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression AAAI 2020 Distilling Neural Networks for Greener and Faster Dependency Parsing ACL 2020 Distilling Portable Generative Adversarial Networks for Image Translation AAAI 2020 Distributed Stochastic Gradient Descent with Event-Triggered Communication AAAI 2020 Distribution-Aware Coordinate Representation for Human Pose Estimation CVPR 2020 DMCP: Differentiable Markov Channel Pruning for Neural Networks CVPR 2020 DNNs as Layers of Cooperating Classifiers AAAI 2020 DNU: Deep Non-Local Unrolling for Computational Spectral Imaging CVPR 2020 Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering EMNLP 2020 Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020 Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help ! EMNLP 2020 Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion CVPR 2020 DropNet: Reducing Neural Network Complexity via Iterative Pruning ICML 2020 DSNAS: Direct Neural Architecture Search Without Parameter Retraining CVPR 2020 Dual Super-Resolution Learning for Semantic Segmentation CVPR 2020 DWM: A Decomposable Winograd Method for Convolution Acceleration AAAI 2020 Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change ACL 2020