[1/4] Excited to share the first experimental release of *torchdyn* github.com/DiffEqML/torch…, a PyTorch library for all things neural differential equations! torchdyn is developed by the core DiffEqML team. Stefano Massaroli DiffeqML

We finally got around to open-sourcing more Neural ODE variants in the "torchdyn" library github.com/DiffEqML/torch…, including our latest "stacked neural ODEs" aka continuous-depth models with piece-wise constant parameters. Michael Poli

[1/5] *torchdyn* goes generative! This new release adds continuous normalizing flows Ricky T. Q. Chen will grathwohl David Duvenaud , energy-based Neural DEs Miles Cranmer Sam Greydanus, and extends existing support for higher-order variants. All powered by Lightning! @_willfalcon

We just open-sourced differentiable SDE solvers in PyTorch: github.com/google-researc… Now you can put stochastic differential equations in your deep learning models, and neural nets in your SDEs! Credit to Xuechen Li.

A new paper from DiffEqML research group! We speed up Neural ODE inference by learning how to solve them efficiently through *hypersolvers* Stefano Massaroli Michael Poli The code will be released soon in torchdyn

As our first NeurIPS experience, I have to say the results surpassed even the wildest of expectations. This is the culmination of a team effort with my dear friend Stefano Massaroli, leading to DiffeqML as an open-source effort for neural differential equations.

Two of our papers got accepted at #NeurIPS2020!!! “Dissecting Neural ODEs” (oral) “Hypersolvers: toward fast continuous depth models” (poster)

“Dissecting Neural ODEs” (#neurips2020 _oral_ paper) unveils the dynamical systems anatomy of continuous-depth learning from back-propagation to depth-varying parameters or state augmentation, while introducing several new models (e.g. data-control, adaptive depth) Michael Poli

[1/6] Announcing **torchdyn version 1.0**: github.com/DiffEqML/torch…! Michael Poli Stefano Massaroli. We roughly doubled the number of tutorials (optimal control, parallel-in-time solvers, hybrid systems), added new models and developed a numerics suite for diff eqs and root finding

The website for our 'The Symbiosis of Deep Learning and Differential Equations' #NeurIPS2021 workshop is up: dl-de.github.io We have a special track for already published papers. Share your work from adjacent fields with the NeurIPS community! Deadline: Sept. 17 AoE

Two papers accepted at #NeurIPS2021: Differentiable Multiple Shooting Layers arxiv.org/abs/2106.03885 Neural Hybrid Automata arxiv.org/abs/2106.04165 Michael Poli Taiji Suzuki Animesh Garg

Let us embark on a fractal journey about dynamical systems and neural implicit representations... 1/

Join us for the second edition of the #NeurIPS2022 workshop "The Symbiosis of Deep Learning and Differential Equations"🌀 We're looking for your AI <> DE ideas: neural diff. eqs., neural operators, diffusion models and novel applications! website: dlde-2022.github.io

❗New deadline for the #NeurIPS2022 workshop❗ "Symbiosis of Deep Learning and Differential Equations": October 1st. Website: dlde-2022.github.io. Send us your work on neural differential equations, learnable numerical methods, continuous-time diffusion and more!