stochastic differential equation

SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates ICML 2020 Stochastic Differential Equations with Variational Wishart Diffusions ICML 2020 Stochastic Gradient and Langevin Processes ICML 2020 The Implicit Regularization of Stochastic Gradient Flow for Least Squares ICML 2020 Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning NeurIPS 2020 A Path Signature Approach for Speech Emotion Recognition Interspeech 2019 AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs ICML 2019 A Solvable High-Dimensional Model of GAN NeurIPS 2019 A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks ICML 2019 Deep learning with differential Gaussian process flows AISTATS 2019 First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise NeurIPS 2019 Learning Deep Stochastic Optimal Control Policies Using Forward-Backward SDEs RSS 2019 Learning interpretable continuous-time models of latent stochastic dynamical systems ICML 2019 Neural Jump Stochastic Differential Equations NeurIPS 2019 Neural Networks with Cheap Differential Operators NeurIPS 2019 Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations JMLR 2019 The Randomized Midpoint Method for Log-Concave Sampling NeurIPS 2019 The Role of Memory in Stochastic Optimization UAI 2019 A Bayesian Perspective on Generalization and Stochastic Gradient Descent ICLR 2018 A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop AISTATS 2018 Black-Box Variational Inference for Stochastic Differential Equations ICML 2018 Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions ICML 2018 Differential Equations for Modeling Asynchronous Algorithms IJCAI 2018 Global Non-convex Optimization with Discretized Diffusions NeurIPS 2018 Acceleration and Averaging in Stochastic Descent Dynamics NeurIPS 2017