Optimization & Theory

Generalized Batch Normalization: Towards Accelerating Deep Neural Networks AAAI 2019 Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression CVPR 2019 Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance IJCAI 2019 Global Sparse Momentum SGD for Pruning Very Deep Neural Networks NeurIPS 2019 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism NeurIPS 2019 Gradient-Based Inference for Networks with Output Constraints AAAI 2019 Gradient descent aligns the layers of deep linear networks ICLR 2019 Gradient Descent Finds Global Minima of Deep Neural Networks ICML 2019 Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds COLT 2019 Gradient Dynamics of Shallow Univariate ReLU Networks NeurIPS 2019 Guided Dropout AAAI 2019 HAQ: Hardware-Aware Automated Quantization With Mixed Precision CVPR 2019 HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs CVPR 2019 High-Dimensional Optimization in Adaptive Random Subspaces NeurIPS 2019 Higher-order Comparisons of Sentence Encoder Representations EMNLP 2019 Hint-Based Training for Non-Autoregressive Machine Translation EMNLP 2019 How degenerate is the parametrization of neural networks with the ReLU activation function? NeurIPS 2019 How do infinite width bounded norm networks look in function space? COLT 2019 Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks NeurIPS 2019 HyperAdam: A Learnable Task-Adaptive Adam for Network Training AAAI 2019 Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior CVPR 2019 Identifying and Controlling Important Neurons in Neural Machine Translation ICLR 2019 Implicit Regularization for Optimal Sparse Recovery NeurIPS 2019 Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks NeurIPS 2019 Importance Estimation for Neural Network Pruning CVPR 2019