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Analyzing and interpreting time-dependent stochastic data requires accurate and robust density estimation.
DeepMoD: Deep learning for Model Discovery in noisy data
Gert-Jan Both, Subham Choudhury, Pierre Sens, and Remy Kusters · 1904
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DeepXDE: A deep learning library for solving differential equations
Lu Lu, Xuhui Meng, Zhiping Mao, and George E. Karniadakis · 1907
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Unconstrained Monotonic Neural Networks
Antoine Wehenkel and Gilles Louppe · 1908
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Non-Parametric Estimation of a Multivariate Probability Density
V. A. Epanechnikov · 1969
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Kernel density estimation via diffusion
Z. I. Botev, J. F. Grotowski, and D. P. Kroese · 2010
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First Steps in Random Walks: From Tools to Applications
J. Klafter and I. M. Sokolov · 2011
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A Review of Kernel Density Estimation with Applications to Econometrics
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Variational Inference with Normalizing Flows
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A review of progress in single particle tracking: from methods to biophysical insights
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, and Lawrence Carin · 2017
Inferring solutions of differential equations using noisy multi-fidelity data
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Glow: Generative Flow with Invertible 1x1 Convolutions
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Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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Neural Ordinary Differential Equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
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Masked Autoregressive Flow for Density Estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Bandwidth Selection in Kernel Density Estimation: A Review
Berwin A Turlach
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Nonparametric Kernel Density Estimation Near the Boundary
Peter Malec and Melanie Schienle
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis
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Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, and Nicolo Fusi
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A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther
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Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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