Optimization & Theory

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model NeurIPS 2019 Width Provably Matters in Optimization for Deep Linear Neural Networks ICML 2019 XNAS: Neural Architecture Search with Expert Advice NeurIPS 2019 You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle NeurIPS 2019 A Biresolution Spectral Framework for Product Quantization CVPR 2018 A Boo(n) for Evaluating Architecture Performance ICML 2018 A Bridging Framework for Model Optimization and Deep Propagation NeurIPS 2018 Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation EMNLP 2018 Accelerating Natural Gradient with Higher-Order Invariance ICML 2018 A Convex Duality Framework for GANs NeurIPS 2018 Adaptive Methods for Nonconvex Optimization NeurIPS 2018 Adaptive Negative Curvature Descent with Applications in Non-convex Optimization NeurIPS 2018 Adaptive Quantization of Neural Networks ICLR 2018 ADMM and Accelerated ADMM as Continuous Dynamical Systems ICML 2018 A Hierarchical Model for Device Placement ICLR 2018 A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping CVPR 2018 Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced NeurIPS 2018 A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication NeurIPS 2018 Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input CVPR 2018 Analyzing Filters Toward Efficient ConvNet CVPR 2018 An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering ACML 2018 An intriguing failing of convolutional neural networks and the CoordConv solution NeurIPS 2018 A PID Controller Approach for Stochastic Optimization of Deep Networks CVPR 2018 Approximating Real-Time Recurrent Learning with Random Kronecker Factors NeurIPS 2018 Approximation Algorithms for Cascading Prediction Models ICML 2018