Which priors matter? Benchmarking models for learning latent dynamics

9 November 2021

Papers citing "Which priors matter? Benchmarking models for learning latent dynamics"

24 / 24 papers shown

Title
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics Katharina Friedl Noémie Jaquier Jens Lundell Tamim Asfour Danica Kragic AI4CE 28 0 0 24 Oct 2024
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains Razmik Arman Khosrovian Takaharu Yaguchi Hiroaki Yoshimura Takashi Matsubara AI4CE 22 0 0 15 Oct 2024
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework Yubo Ye Sumeet Vadhavkar Xiajun Jiang R. Missel Huafeng Liu Linwei Wang 31 0 0 13 Mar 2024
eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling Matthew Dowling Yuan Zhao Il Memming Park BDL 30 5 0 03 Mar 2024
Stability-Informed Initialization of Neural Ordinary Differential Equations Theodor Westny Arman Mohammadi Daniel Jung Erik Frisk 23 0 0 27 Nov 2023
Hamiltonian GAN Christine Allen-Blanchette GAN AI4CE 27 1 0 22 Aug 2023
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers Kai Lagemann C. Lagemann Swarnava Mukherjee 34 2 0 21 Jun 2023
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs Zhongkai Hao J. Yao Chang Su Hang Su Ziao Wang ... Zeyu Xia Yichi Zhang Songming Liu Lu Lu Jun Zhu PINN 29 29 0 15 Jun 2023
Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets Aiqing Zhu Tom S. Bertalan Beibei Zhu Yifa Tang Ioannis G. Kevrekidis 21 5 0 31 Mar 2023
Knowledge-augmented Deep Learning and Its Applications: A Survey Zijun Cui Tian Gao Kartik Talamadupula Qiang Ji 25 17 0 30 Nov 2022
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations Simon Koop M. Peletier J. Portegies Vlado Menkovski DiffM 24 1 0 17 Nov 2022
FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities Takashi Matsubara Takaharu Yaguchi PINN 14 7 0 01 Oct 2022
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images Rembert Daems Jeroen Taets Francis Wyffels Guillaume Crevecoeur 11 1 0 22 Jun 2022
CD $^2$ : Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance Rongfei Zeng Mai Su Ruiyun Yu Xingwei Wang 3DV 18 2 0 01 Jun 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 29 84 0 13 Apr 2022
Learning Trajectories of Hamiltonian Systems with Neural Networks Katsiaryna Haitsiukevich Alexander Ilin 17 4 0 11 Apr 2022
Improving Generalization via Uncertainty Driven Perturbations Matteo Pagliardini Gilberto Manunza Martin Jaggi Michael I. Jordan Tatjana Chavdarova AAML AI4CE 11 4 0 11 Feb 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks Nate Gruver Marc Finzi Samuel Stanton A. Wilson AI4CE 13 39 0 10 Feb 2022
Hamiltonian latent operators for content and motion disentanglement in image sequences Asif Khan Amos Storkey 16 2 0 02 Dec 2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 37 7 0 10 Nov 2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models M. Lutter Jan Peters PINN AI4CE 31 39 0 05 Oct 2021
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels Austin Stone Oscar Ramirez K. Konolige Rico Jonschkowski 129 101 0 07 Jan 2021
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 146 219 0 29 Sep 2019
Building machines that adapt and compute like brains Brenden Lake J. Tenenbaum AI4CE FedML NAI AILaw 254 890 0 11 Nov 2017