Fetching the paper…
Reading the bibliography…
The conjoining of dynamical systems and deep learning has become a topic of great interest.
“Language Models are Few-Shot Learners”
Tom Brown et al · 1901
Earlier work this paper cites.
“Forecasting seasonals and trends by exponentially weighted moving averages”
C. Holt · 1957
Earlier work this paper cites.
“Forecasting sales by exponentially weighted moving averages”
P. Winters · 1960
Earlier work this paper cites.
“The mathematical theory of optimal processes”, 1962
L.. Pontryagin, E.. Mishchenko, V.. Boltyanskii and R.. Gamkrelidze · 1962
Earlier work this paper cites.
“The Pricing of Options and Corporate Liabilities”
F. Black and M. Scholes · 1973
Earlier work this paper cites.
“A family of embedded Runge–Kutta formulae”
J.. Dormand and P.. Prince · 1980
Earlier work this paper cites.
“Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”
Robert. Engle · 1982
Earlier work this paper cites.
“Recursive Estimation of Mixed Autoregressive-Moving Average Order”
E.. Hannan and J. Rissanen · 1982
Earlier work this paper cites.
“A theory of term structure of interest rates”
J.. Cox, J.. Ingersoll and S.. Ross · 1985
Earlier work this paper cites.
“Generalized Autoregressive Conditional Heteroskedasticity”
Tim Bollerslev · 1986
Earlier work this paper cites.
“A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines”
Michael Hutchinson · 1989
Earlier work this paper cites.
“A Neural Network Approach for Identification of Continuous-Time Nonlinear Dynamic Systems”
S. Chu and Rahmat Shoureshi · 1991
Earlier work this paper cites.
“Achieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation”
A. Griewank · 1992
Earlier work this paper cites.
“Numerical Solution of Stochastic Differential Equations”
P.. Kloeden and E. Platen · 1992
Earlier work this paper cites.
“Discrete-vs. continuous-time nonlinear signal processing of Cu electrodissolution data”
R. Rico-Martínez et al · 1992
Earlier work this paper cites.
“Multilayer feedforward networks with a nonpolynomial activation function can approximate any function”
Moshe Leshno, Vladimir. Lin, Allan Pinkus and Shimon Schocken · 1993
Earlier work this paper cites.
“Continuous time modeling of nonlinear systems: a neural network-based approach”
R. Rico-Martinez and I.G. Kevrekidis · 1993
Earlier work this paper cites.
“Continuous-time nonlinear signal processing: a neural network based approach for gray box identification”
R. Rico-Martinez, J.S. Anderson and I.G. Kevrekidis · 1994
Earlier work this paper cites.
“Variable step size control in the numerical solution of stochastic differential equations”
J. Gaines and T. Lyons · 1997
Earlier work this paper cites.
“Long short-term memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
“Artificial Neural Networks for Solving Ordinary and Partial Differential Equations”
I.. Lagaris, A. Likas and D.. Fotiadis · 1997
Earlier work this paper cites.
“Artificial neural network methods in quantum mechanics”
I.E. Lagaris, A. Likas and D.I. Fotiadis · 1997
Earlier work this paper cites.
“Differential equations driven by rough signals.”
Terry. Lyons · 1998
Earlier work this paper cites.
“Approximation theory of the MLP model in neural networks”
Allan Pinkus · 1999
Earlier work this paper cites.
“Runge-Kutta methods in optimal control and the transformed adjoint system”
William. Hager · 2000
Earlier work this paper cites.
“Interest Rate Models: Theory and Practice”
D. Brigo and F. Mercurio · 2001
Earlier work this paper cites.
“Solving Ordinary Differential Equations II Stiff and Differential-Algebraic Problems”
E. Hairer and G. Wanner · 2002
Earlier work this paper cites.
“A famous nonlinear stochastic equation (Lotka-Volterra model with diffusion)”
M. Arató · 2003
Earlier work this paper cites.
“Population Growth as a Nonlinear Stochastic Process”
T.. Soboleva and A.. Pleasants · 2003
Earlier work this paper cites.
“Rough paths, Signatures and the modelling of functions on streams”
Terry Lyons · 2004
Earlier work this paper cites.
“Differential equations driven by rough paths”
Terry Lyons, Michael Caruana and Thierry Lévy · 2004
Earlier work this paper cites.
“Cvodes, the sensitivity-enabled ode solver in sundials”
Radu Serban and Alan Hindmarsh · 2005
Earlier work this paper cites.
“Smoking adjoints: fast Monte Carlo Greeks”
Mike Giles and Paul Glasserman · 2006
Earlier work this paper cites.
“On Wright-Fisher diffusion and its relatives”
T. Huillet · 2007
Earlier work this paper cites.
“Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation”
A. Griewank and A. Walther · 2008
Earlier work this paper cites.
“Solving Ordinary Differential Equations I Nonstiff problems”
E. Hairer, S.P. Nørsett and G. Wanner · 2008
Earlier work this paper cites.
“The Magnus expansion and some of its applications”
Sergio Blanes, Fernando Casas, Jose-Angel Oteo and Jose Ros · 2009
Earlier work this paper cites.
“Stability of the leapfrog/midpoint method”
L.F. Shampine · 2009
Earlier work this paper cites.
“Multidimensional stochastic processes as rough paths: theory and applications”
Peter. Friz and Nicolas. Victoir · 2010
Earlier work this paper cites.
“Uniqueness for the signature of a path of bounded variation and the reduced path group”
Ben. Hambly and Terry. Lyons · 2010
Earlier work this paper cites.
“Copula Processes”
A.. Wilson and Z. Ghahramani · 2010
Earlier work this paper cites.
“Parallel random numbers: as easy as 1, 2, 3.”
J.. Salmon, M.. Moraes, R.. Dror and D.. Shaw · 2011
Earlier work this paper cites.
“Runge–Kutta pairs of order 5(4) satisfying only the first column simplifying assumption”
Ch. Tsitouras · 2011
Earlier work this paper cites.
“The Langevin Equation: With Applications to Stochastic Problems in Physics, Chemistry and Electrical Engineering”
W.. Coffey, Y.. Kalmykov and J.. Waldron · 2012
Earlier work this paper cites.
“ADADELTA: An Adaptive Learning Rate Method”
Matthew. Zeiler · 2012
Earlier work this paper cites.
“Splittable pseudorandom number generators using cryptographic hashing”
K. Claessen and M. Pałka · 2013
Earlier work this paper cites.
“A kernel two-sample test”
A. Gretton et al · 2013
Earlier work this paper cites.
“Learning from the past, predicting the statistics for the future, learning an evolving system”
Daniel Levin, Terry Lyons and Hao Ni · 2013
Earlier work this paper cites.
“An asynchronous leapfrog method II”
Ulrich Mutze · 2013
Earlier work this paper cites.
“Continuous martingales and Brownian motion”
D. Revuz and M. Yor · 2013
Earlier work this paper cites.
“Prioritized Grammar Enumeration: Symbolic Regression by Dynamic Programming”
Tony Worm and Kenneth Chiu · 2013
Earlier work this paper cites.
“Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation”
Kyunghyun Cho et al · 2014
Earlier work this paper cites.
“Generative Adversarial Nets”
I. Goodfellow et al · 2014
Earlier work this paper cites.
“On the notion(s) of duality for Markov processes”
Sabine Jansen and Noemi Kurt · 2014
Earlier work this paper cites.
“Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations”
G.. Pavliotis · 2014
Earlier work this paper cites.
“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2015
Earlier work this paper cites.
“Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
D Kingma and J Ba · 2015
Earlier work this paper cites.
“Autoencoding beyond pixels using a learned similarity metric”
Anders Larsen, Søren Sønderby, Hugo Larochelle and Ole Winther · 2015
Earlier work this paper cites.
“Variational Inference with Normalizing Flows”
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
“U-Net: Convolutional Networks for Biomedical Image Segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
Jimmy Ba, Jamie Kiros and Geoffrey. Hinton · 2016
Earlier work this paper cites.
“Discovering governing equations from data by sparse identification of nonlinear dynamical systems”
Steven. Brunton, Joshua. Proctor and J. Kutz · 2016
Earlier work this paper cites.
“Numerical Methods for Ordinary Differential Equations”, 2016
J.. Butcher · 2016
Earlier work this paper cites.
“A primer on the signature method in machine learning”
I. Chevyrev and A. Kormilitzin · 2016
Earlier work this paper cites.
“Gaussian Error Linear Units (GELUs)”
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
“Partial differential equations and stochastic methods in molecular dynamics”
X. Lelièvre and G. Stoltz · 2016
Earlier work this paper cites.
“Reynolds averaged turbulence modelling using deep neural networks with embedded invariance”
Julia Ling, Andrew Kurzawski and Jeremy Templeton · 2016
Earlier work this paper cites.
“Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics”
Niall. Mangan, Steven. Brunton, Joshua. Proctor and J. Kutz · 2016
Earlier work this paper cites.
“Extrapolation and learning equations”
Georg Martius and Christoph. Lampert · 2016
Earlier work this paper cites.
“OptNet: Differentiable Optimization as a Layer in Neural Networks”
Brandon Amos and J. Kolter · 2017
Earlier work this paper cites.
“Input Convex Neural Networks”
Brandon Amos, Lei Xu and J. Kolter · 2017
Earlier work this paper cites.
“Wasserstein Generative Adversarial Networks”
M. Arjovsky, S. Chintala and L. Bottou · 2017
Earlier work this paper cites.
“Julia: A fresh approach to numerical computing”
Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral Shah · 2017
Earlier work this paper cites.
“From optimal transport to generative modeling: the VEGAN cookbook”
Olivier Bousquet et al · 2017
Earlier work this paper cites.
“Recurrent Batch Normalization”
Tim Cooijmans et al · 2017
Earlier work this paper cites.
“Density estimation using Real NVP”
Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2017
Earlier work this paper cites.
“A Proposal on Machine Learning via Dynamical Systems”
Weinan E · 2017
Earlier work this paper cites.
“Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning”
Stefan Elfwing, Eiji Uchibe and Kenji Doya · 2017
Earlier work this paper cites.
“Hands-on machine learning with Scikit-Learn and TensorFlow”
Aurélien Géron · 2017
Earlier work this paper cites.
“The Reversible Residual Network: Backpropagation Without Storing Activations”
Aidan. Gomez, Mengye Ren, Raquel Urtasun and Roger. Grosse · 2017
Earlier work this paper cites.
“Improved Training of Wasserstein GANs”
I. Gulrajani et al · 2017
Earlier work this paper cites.
“Stable Architectures for Deep Neural Networks”
Eldad Haber and Lars Ruthotto · 2017
Earlier work this paper cites.
“Approximating Continuous Functions by ReLU Nets of Minimal Width”
Boris Hanin and Mark Sellke · 2017
Earlier work this paper cites.
“GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium”
Martin Heusel et al · 2017
Earlier work this paper cites.
“MMD GAN: Towards Deeper Understanding of Moment Matching Network”
C.-L. Li et al · 2017
Earlier work this paper cites.
Yiping Lu, Aoxiao Zhong, Quanzheng Li and Bin Dong · 2017
Earlier work this paper cites.
“The Expressive Power of Neural Networks: A View from the Width”
Zhou Lu et al · 2017
Earlier work this paper cites.
“Searching for Activation Functions”
Prajit Ramachandran, Barret Zoph and Quoc. Le · 2017
Earlier work this paper cites.
“Calculation of Iterated-Integral Signatures and Log Signatures”
Jeremy Reizenstein · 2017
Earlier work this paper cites.
“Variational Approaches for Auto-Encoding Generative Adversarial Networks”
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley and Shakir Mohamed · 2017
Earlier work this paper cites.
“Data-driven discovery of partial differential equations”
Samuel. Rudy, Steven. Brunton, Joshua. Proctor and J. Kutz · 2017
Earlier work this paper cites.
“Attention is All you Need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data”
Jian-Xun Wang, Jin-Long Wu and Heng Xiao · 2017
Earlier work this paper cites.
“PolyNet: A Pursuit of Structural Diversity in Very Deep Networks”
Xingcheng Zhang, Zhizhong Li, Chen Loy and Dahua Lin · 2017
Earlier work this paper cites.
“Demystifying MMD GANs”
Mikołaj Bińkowski, Danica. Sutherland, Michael Arbel and Arthur Gretton · 2018
Earlier work this paper cites.
“JAX: composable transformations of Python+NumPy programs”, 2018
James Bradbury et al · 2018
Earlier work this paper cites.
“Reversible Architectures for Arbitrarily Deep Residual Neural Networks”
Bo Chang et al · 2018
Earlier work this paper cites.
“Recurrent Neural Networks for Multivariate Time Series with Missing Values”
Zhengping Che et al · 2018
Cited alongside, same era.
“torchdiffeq”, 2018
Ricky.. Chen · 2018
Cited alongside, same era.
“Neural Ordinary Differential Equations”
Ricky.. Chen, Yulia Rubanova, Jesse Bettencourt and David Duvenaud · 2018
Cited alongside, same era.
“Solving high-dimensional partial differential equations using deep learning”
Jiequn Han, Arnulf Jentzen and Weinan E · 2018
Cited alongside, same era.
“Averaging Weights Leads to Wider Optima and Better Generalization”
Pavel Izmailov et al · 2018
Cited alongside, same era.
“Neural Tangent Kernel: Convergence and Generalization in Neural Networks”
Arthur Jacot, Franck Gabriel and Clement Hongler · 2018
Cited alongside, same era.
“Neural Manifold Ordinary Differential Equations”
Aaron Lou et al · 2020
Later among the works it cites.
“Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation”
Dimitra Maoutsa, Sebastian Reich and Manfred Opper · 2020
Later among the works it cites.
“Dissecting Neural ODEs”
Stefano Massaroli et al · 2020
Later among the works it cites.
“Riemannian Continuous Normalizing Flows”
Emile Mathieu and Maximilian Nickel · 2020
Later among the works it cites.
“A Generalised Signature Method for Time Series”
James Morrill, Adeline Fermanian, Patrick Kidger and Terry Lyons · 2020
Later among the works it cites.
“On Second Order Behaviour in Augmented Neural ODEs”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Progressive Growing of GANs for Improved Quality, Stability, and Variation”
Tero Karras, Timo Aila, Samuli Laine and Jaakko Lehtinen · 2018
Cited alongside, same era.
“Normalizing Flows” https://akosiorek.github.io/ml/2018/04/03/norm_flows.html , 2018
Adam Kosiorek · 2018
Cited alongside, same era.
“Are GANs Created Equal? A Large-Scale Study”
Mario Lucic et al · 2018
Cited alongside, same era.
“Deep learning for universal linear embeddings of nonlinear dynamics”
Bethany Lusch, J. Kutz and Steven. Brunton · 2018
Cited alongside, same era.
“Neural Networks Trained to Solve Differential Equations Learn General Representations”
Martin Magill, Faisal Qureshi and Hendrick de Haan · 2018
Cited alongside, same era.
“Spectral Normalization for Generative Adversarial Networks”
T. Miyato, T. Kataoka, M. Koyama and Y. Yoshida · 2018
Cited alongside, same era.
Alexander Norcliffe et al · 2020
Later among the works it cites.
“Stochasticity in Neural ODEs: An Empirical Study”
Viktor Oganesyan, Alexandra Volokhova and Dmitry Vetrov · 2020
Later among the works it cites.
Derek Onken and Lars Ruthotto · 2020
Later among the works it cites.
“Hypersolvers: Toward Fast Continuous-Depth Models”
Michael Poli et al · 2020
Later among the works it cites.
Christopher Rackauckas et al · 2020
Later among the works it cites.
“Universal Differential Equations for Scientific Machine Learning”
Christopher Rackauckas et al · 2020
Later among the works it cites.
“Capturing missing physics in climate model parameterizations using neural differential equations”
Ali Ramadhan et al · 2020
Later among the works it cites.
“Hopfield Networks is All You Need”
Hubert Ramsauer et al · 2020
Later among the works it cites.
“The Signature Kernel is the solution of a Goursat PDE”
Cristopher Salvi et al · 2020
Later among the works it cites.
“Deep Learning with PyTorch”
Eli Stevens, Luca Antiga and Thomas Viehmann · 2020
Later among the works it cites.
“Monash University, UEA, UCR Time Series Regression Archive”, 2020
Chang Tan et al · 2020
Later among the works it cites.
“Universal Approximation Property of Neural Ordinary Differential Equations”
Takeshi Teshima et al · 2020
Later among the works it cites.
“Monotone operator equilibrium networks”
Ezra Winston and J. Kolter · 2020
Later among the works it cites.
“Approximation Capabilities of Neural ODEs and Invertible Residual Networks”
Han Zhang, Xi Gao, Jacob Unterman and Tom Arodz · 2020
Later among the works it cites.
“Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning”
Yaofeng Zhong, Biswadip Dey and Amit Chakraborty · 2020
Later among the works it cites.
“Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control”
Yaofeng Zhong, Biswadip Dey and Amit Chakraborty · 2020
Later among the works it cites.
“AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients”
Juntang Zhuang et al · 2020
Later among the works it cites.
“Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE”
Juntang Zhuang et al · 2020
Later among the works it cites.
“Stabilizing Equilibrium Models by Jacobian Regularization”
Shaojie Bai, Vladlen Koltun and Zico Kolter · 2021
Later among the works it cites.
“Consistency of mechanistic causal discovery in continuous-time using Neural ODEs”
Alexis Bellot, Kim Branson and Mihaela van Schaar · 2021
Later among the works it cites.
“Efficient and Modular Implicit Differentiation”
Mathieu Blondel et al · 2021
Later among the works it cites.
“Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling”
Valentin Bortoli, James Thornton, Jeremy Heng and Arnaud Doucet · 2021
Later among the works it cites.
“General Signature Kernels”
Thomas Cass, Terry Lyons and Xingcheng Xu · 2021
Later among the works it cites.
“GRAND: Graph Neural Diffusion”
Ben Chamberlain et al · 2021
Later among the works it cites.
“Learning Neural Event Functions for Ordinary Differential Equations”
Ricky.. Chen, Brandon Amos and Maximilian Nickel · 2021
Later among the works it cites.
“Neural Spatio-Temporal Point Processes”
Ricky.. Chen, Brandon Amos and Maximilian Nickel · 2021
Later among the works it cites.
“Arbitrage-free neural-SDE market models”
Samuel. Cohen, Christoph Reisinger and Sheng Wang · 2021
Later among the works it cites.
Miles Cranmer, Private communication, 2021
2021
Later among the works it cites.
Miles Cranmer, Private communication, 2021
2021
Later among the works it cites.
“Continuous Latent Process Flows”
Ruizhi Deng, Marcus. Brubaker, Greg Mori and Andreas. Lehrmann · 2021
Later among the works it cites.
“Diffusion Models Beat GANs on Image Synthesis”
Prafulla Dhariwal and Alex Nichol · 2021
Later among the works it cites.
“Framing RNN as a kernel method: A neural ODE approach”
Adeline Fermanian, Pierre Marion, Jean-Philippe Vert and Gérard Biau · 2021
Later among the works it cites.
Pete Florence et al · 2021
Later among the works it cites.
“Decomposing reverse-mode automatic differentiation”
Roy Frostig et al · 2021
Later among the works it cites.
“Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop”
Samy Fung et al · 2021
Later among the works it cites.
“User Documentation for CVODES v5.7.0”, 2021
Alan. Hindmarsh et al · 2021
Later among the works it cites.
“Cascaded Diffusion Models for High Fidelity Image Generation”
Jonathan Ho et al · 2021
Later among the works it cites.
“A Variational Perspective on Diffusion-Based Generative Models and Score Matching”
Chin-Wei Huang, Jae Lim and Aaron Courville · 2021
Later among the works it cites.
“Climate Modeling with Neural Diffusion Equations”
Jeehyun Hwang et al · 2021
Later among the works it cites.
“Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting”
Sheo Jhin et al · 2021
Later among the works it cites.
“Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks”
Weiqi Ji, Franz Richter, Michael. Gollner and Sili Deng · 2021
Later among the works it cites.
“Promoting global stability in data-driven models of quadratic nonlinear dynamics”
Alan. Kaptanoglu et al · 2021
Later among the works it cites.
“Learning Differential Equations that are Easy to Solve”
Jacob Kelly, Jesse Bettencourt, Matthew Johnson and David Duvenaud · 2021
Later among the works it cites.
“Diffrax”, 2021
Patrick Kidger · 2021
Later among the works it cites.
“Equinox”, 2021
Patrick Kidger · 2021
Later among the works it cites.
“sympytorch”, 2021
Patrick Kidger · 2021
Later among the works it cites.
“torchtyping”, 2021
Patrick Kidger · 2021
Later among the works it cites.
““Hey, that’s not an ODE”: Faster ODE Adjoints via Seminorms”
Patrick Kidger, Ricky.. Chen and Terry Lyons · 2021
Later among the works it cites.
“Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU”
Patrick Kidger and Terry Lyons · 2021
Later among the works it cites.
“Efficient and Accurate Gradients for Neural SDEs”
Patrick Kidger, James Foster, Xuechen Li and Terry Lyons · 2021
Later among the works it cites.
“Neural SDEs as Infinite-Dimensional GANs”
Patrick Kidger, James Foster, Xuechen Li and Terry Lyons · 2021
Later among the works it cites.
“Stiff neural ordinary differential equations”
Suyong Kim et al · 2021
Later among the works it cites.
“Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations”
Timothy. Kim, Thomas. Luo, Jonathan. Pillow and Carlos Brody · 2021
Later among the works it cites.
“Variational Diffusion Models”
Diederik. Kingma, Tim Salimans, Ben Poole and Jonathan Ho · 2021
Later among the works it cites.
“Fourier Neural Operator for Parametric Partial Differential Equations”
Zongyi Li et al · 2021
Later among the works it cites.
“Complex Momentum for Optimization in Games”
Jonathan Lorraine, David Acuna, Paul Vicol and David Duvenaud · 2021
Later among the works it cites.
“Implicit Normalizing Flows”
Cheng Lu et al · 2021
Later among the works it cites.
“Value Iteration in Continuous Actions, States and Time”
Michael Lutter et al · 2021
Later among the works it cites.
“Differentiable Multiple Shooting Layers”
Stefano Massaroli et al · 2021
Later among the works it cites.
“SDEdit: Image Synthesis and Editing with Stochastic Differential Equations”
Chenlin Meng et al · 2021
Later among the works it cites.
“Neural Controlled Differential Equations for Online Prediction Tasks”
James Morrill, Patrick Kidger, Lingyi Yang and Terry Lyons · 2021
Later among the works it cites.
“Neural Rough Differential Equations for Long Time Series”
James Morrill et al · 2021
Later among the works it cites.
“Neural ODE Processes”
Alexander Norcliffe et al · 2021
Later among the works it cites.
“OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport”
Derek Onken, Samy Wu, Xingjian Li and Lars Ruthotto · 2021
Later among the works it cites.
“ResNet After All: Neural ODEs and Their Numerical Solution”
Katharina Ott, Prateek Katiyar, Philipp Hennig and Michael Tiemann · 2021
Later among the works it cites.
“Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics”
Avik Pal, Yingbo Ma, Viral Shah and Christopher Rackauckas · 2021
Later among the works it cites.
“Minimum Width for Universal Approximation”
Sejun Park, Chulhee Yun, Jaeho Lee and Jinwoo Shin · 2021
Later among the works it cites.
“Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions”
Michael Poli et al · 2021
Later among the works it cites.
Felix Pollock, Private communication, 2021
2021
Later among the works it cites.
“Continuous-in-Depth Neural Networks”
Alejandro. Queiruga, N. Erichson, Dane Taylor and Michael. Mahoney · 2021
Later among the works it cites.
“Notes on Algorithms” https://devdocs.sciml.ai/dev/internals/notes_on_algorithms/ , 2021
Christopher Rackauckas · 2021
Later among the works it cites.
“Timestepping Method Descriptions” https://diffeq.sciml.ai/stable/extras/timestepping/ , 2021
Christopher Rackauckas · 2021
Later among the works it cites.
“Learning Transferable Visual Models From Natural Language Supervision”
Alec Radford et al · 2021
Later among the works it cites.
“Collocation based training of neural ordinary differential equations”
Elisabeth Roesch, Christopher Rackauckas and Michael.. Stumpf · 2021
Later among the works it cites.
“Moser Flow: Divergence-based Generative Modeling on Manifolds”
Noam Rozen, Aditya Grover, Maximilian Nickel and Yaron Lipman · 2021
Later among the works it cites.
“Neural Stochastic Partial Differential Equations”
Cristopher Salvi, Maud Lemercier and Andris Gerasimovics · 2021
Later among the works it cites.
“Momentum Residual Neural Networks”
Michael. Sander, Pierre Ablin, Mathieu Blondel and Gabriel Peyré · 2021
Later among the works it cites.
“The Uncanny Similarity of Recurrence and Depth”
Avi Schwarzschild et al · 2021
Later among the works it cites.
“Learning Gradient Fields for Molecular Conformation Generation”
Chence Shi, Shitong Luo, Minkai Xu and Jian Tang · 2021
Later among the works it cites.
“Segmenting Hybrid Trajectories using Latent ODEs”
Ruian Shi and Quaid Morris · 2021
Later among the works it cites.
“Maximum Likelihood Training of Score-Based Diffusion Models”
Yang Song, Conor Durkan, Iain Murray and Stefano Ermon · 2021
Later among the works it cites.
“Score-Based Generative Modeling through Stochastic Differential Equations”
Yang Song et al · 2021
Later among the works it cites.
“Physics-based Deep Learning”
Nils Thuerey et al · 2021
Later among the works it cites.
“Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections”
Csaba Toth, Patric Bonnier and Harald Oberhauser · 2021
Later among the works it cites.
Benjamin Walker, Private communication, 2021
2021
Later among the works it cites.
“Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems”
Rui Wang et al · 2021
Later among the works it cites.
“Ab-initio study of interacting fermions at finite temperature with neural canonical transformation”
Hao Xie, Linfeng Zhang and Lei Wang · 2021
Later among the works it cites.
“Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting”
Yuan Yin et al · 2021
Later among the works it cites.
“Diffusion Normalizing Flow”
Qinsheng Zhang and Yongxin Chen · 2021
Later among the works it cites.
“Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models”
Yaofeng Zhong, Biswadip Dey and Amit Chakraborty · 2021
Later among the works it cites.
“MALI: A memory efficient and reverse accurate integrator for Neural ODEs”
Juntang Zhuang, Nicha. Dvornek, Sekhar Tatikonda and James. Duncan · 2021
Later among the works it cites.
“NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations”
Kirill Zubov et al · 2021
Later among the works it cites.