Optimization & Theory

Stochastic Gradient Push for Distributed Deep Learning ICML 2019 Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR 2019 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NeurIPS 2019 Stochastic Variance-Reduced Cubic Regularization Methods JMLR 2019 Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation CVPR 2019 Structured Knowledge Distillation for Semantic Segmentation CVPR 2019 Structured Pruning of Neural Networks With Budget-Aware Regularization CVPR 2019 Super Sparse Convolutional Neural Networks AAAI 2019 Surfing: Iterative Optimization Over Incrementally Trained Deep Networks NeurIPS 2019 Taking a Deeper Look at the Inverse Compositional Algorithm CVPR 2019 Taming the Noisy Gradient: Train Deep Neural Networks with Small Batch Sizes IJCAI 2019 Tangent-Normal Adversarial Regularization for Semi-Supervised Learning CVPR 2019 TAPAS: Train-Less Accuracy Predictor for Architecture Search AAAI 2019 TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding AAAI 2019 The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization EMNLP 2019 The Geometry of Deep Networks: Power Diagram Subdivision NeurIPS 2019 The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes AAAI 2019 The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks NeurIPS 2019 Theoretical Analysis of Auto Rate-Tuning by Batch Normalization ICLR 2019 Tightness-Aware Evaluation Protocol for Scene Text Detection CVPR 2019 TIGS: An Inference Algorithm for Text Infilling with Gradient Search ACL 2019 Towards a Better Understanding and Regularization of GAN Training Dynamics UAI 2019 Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation EMNLP 2019 Towards a Theoretical Understanding of Hashing-Based Neural Nets AISTATS 2019 Towards Optimal Structured CNN Pruning via Generative Adversarial Learning CVPR 2019