Co-occurring keywords · papers mentioning both
Papers
Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
ICML 2021
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
ICML 2021
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
NeurIPS 2021
Implicit Langevin Algorithms for Sampling From Log-concave Densities
JMLR 2021
Large-Scale Multi-Agent Deep FBSDEs
ICML 2021
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
AISTATS 2021
Moment-Based Variational Inference for Stochastic Differential Equations
AISTATS 2021
Neural SDEs as Infinite-Dimensional GANs
ICML 2021
Neural TMDlayer: Modeling Instantaneous Flow of Features via SDE Generators
ICCV 2021
Noisy Recurrent Neural Networks
NeurIPS 2021
On Smoother Attributions using Neural Stochastic Differential Equations
IJCAI 2021
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
NeurIPS 2021
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation
NeurIPS 2021
Score-Based Generative Modeling through Stochastic Differential Equations
ICLR 2021
Stochastic continuous normalizing flows: training SDEs as ODEs
UAI 2021
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations
JMLR 2021
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
JMLR 2020
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework
CVPR 2020
Infinitely deep neural networks as diffusion processes
AISTATS 2020
Monotonic Gaussian Process Flows
AISTATS 2020
On the Generalization Benefit of Noise in Stochastic Gradient Descent
ICML 2020
Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications
JMLR 2020
Predicting Training Time Without Training
NeurIPS 2020
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
NeurIPS 2020
Scalable Gradients for Stochastic Differential Equations
AISTATS 2020