Optimization & Theory

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization ICML 2020 Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines CVPR 2020 Harmonious Coexistence of Structured Weight Pruning and Ternarization for Deep Neural Networks AAAI 2020 Hashing Based Answer Selection AAAI 2020 HAT: Hardware-Aware Transformers for Efficient Natural Language Processing ACL 2020 HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks NeurIPS 2020 HDK: Toward High-Performance Deep-Learning-Based Kirchhoff Analysis AAAI 2020 Heterogeneous Knowledge Distillation Using Information Flow Modeling CVPR 2020 Hierarchical Knowledge Squeezed Adversarial Network Compression AAAI 2020 Hierarchically Robust Representation Learning CVPR 2020 Hierarchical Modes Exploring in Generative Adversarial Networks AAAI 2020 Hierarchical nucleation in deep neural networks NeurIPS 2020 High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks CVPR 2020 High Performance Depthwise and Pointwise Convolutions on Mobile Devices AAAI 2020 High Performance Natural Language Processing EMNLP 2020 Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection CVPR 2020 HLHLp: Quantized Neural Networks Training for Reaching Flat Minima in Loss Surface AAAI 2020 How Good is SGD with Random Shuffling? COLT 2020 HRank: Filter Pruning Using High-Rank Feature Map CVPR 2020 Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization ICML 2020 Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function NeurIPS 2020 HyperSTAR: Task-Aware Hyperparameters for Deep Networks CVPR 2020 Identifying Model Weakness with Adversarial Examiner AAAI 2020 ILFO: Adversarial Attack on Adaptive Neural Networks CVPR 2020 Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process COLT 2020