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Neural SDEs combine many of the best qualities of both RNNs and SDEs: memory efficient training, high-capacity function approximation, and strong priors on model space.
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Continuous martingales and Brownian motion , volume 293
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Gaussian Error Linear Units (GELUs)
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Improved Training of Wasserstein GANs
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Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
S. Elfwing, E. Uchibe, and K. Doya · 2017
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The Unusual Effectiveness of Averaging in GAN training
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SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
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L. Hodgkinson, C. van der Heide, F. Roosta, and M. Mahoney · 2020
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Robust Pricing and Hedging via Neural SDEs
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P. Ramachandran, B. Zoph, and Q. Le · 2017
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Wasserstein Generative Adversarial Networks
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MMD GAN: Towards Deeper Understanding of Moment Matching Network
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UCI Machine Learning Repository, 2017
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Cautionary tales on air-quality improvement in Beijing
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Spectral Normalization for Generative Adversarial Networks
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Neural Ordinary Differential Equations
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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
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Analyzing and Improving the Image Quality of StyleGAN
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An optimal polynomial approximation of Brownian motion
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Numerical approximations for stochastic differential equations
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Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
C. Toth and H. Oberhauser · 2020
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A generalised signature method for multivariate time series feature extraction
J. Morrill, A. Fermanian, P. Kidger, and T. Lyons · 2020
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torchcde
P. Kidger · 2020
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Neural SDEs as Infinite-Dimensional GANs
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Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
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Score-Based Generative Modeling through Stochastic Differential Equations
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Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
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MALI: A memory efficient and reverse accurate integrator for Neural ODEs
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Momentum Residual Neural Networks
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Neural Rough Differential Equations for Long Time Series
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torchtyping
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The Signature Kernel is the solution of a Goursat PDE
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SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
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Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
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