Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators

20 June 2020

Papers citing "Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators"

26 / 26 papers shown

Title
Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices Yuka Hashimoto Tomoharu Iwata 28 0 0 03 Oct 2024
Transport of Algebraic Structure to Latent Embeddings Samuel Pfrommer Brendon G. Anderson Somayeh Sojoudi 37 0 0 27 May 2024
Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees Sean Jaffe A. Davydov Deniz Lapsekili Ambuj K. Singh Francesco Bullo 40 2 0 12 Feb 2024
A Flow-Based Generative Model for Rare-Event Simulation Lachlan J. Gibson Marcus Hoerger Dirk P. Kroese 21 4 0 13 May 2023
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems Fabian Altekrüger Paul Hagemann Gabriele Steidl TPM 31 9 0 28 Mar 2023
Certified Invertibility in Neural Networks via Mixed-Integer Programming Tianqi Cui Tom S. Bertalan George J. Pappas M. Morari Ioannis G. Kevrekidis Mahyar Fazlyab AAML 27 2 0 27 Jan 2023
Whitening Convergence Rate of Coupling-based Normalizing Flows Felix Dräxler Christoph Schnörr Ullrich Kothe 41 7 0 25 Oct 2022
Transfer learning with affine model transformation Shunya Minami Kenji Fukumizu Yoshihiro Hayashi Ryo Yoshida 27 1 0 18 Oct 2022
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks Valentin Duruisseaux J. Burby Q. Tang 40 11 0 11 Oct 2022
Invertible Rescaling Network and Its Extensions Mingqing Xiao Shuxin Zheng Chang-Shu Liu Zhouchen Lin Tie-Yan Liu 34 27 0 09 Oct 2022
Reliable amortized variational inference with physics-based latent distribution correction Ali Siahkoohi G. Rizzuti Rafael Orozco Felix J. Herrmann 36 28 0 24 Jul 2022
Variational Flow Graphical Model Shaogang Ren Belhal Karimi Dingcheng Li Ping Li 27 4 0 06 Jul 2022
Gradual Domain Adaptation via Normalizing Flows Shogo Sagawa H. Hino CLL OOD 22 10 0 23 Jun 2022
Identifiability of deep generative models without auxiliary information Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam DRL 26 49 0 20 Jun 2022
Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks M. Reh M. Gärttner 27 8 0 04 Jun 2022
Flow-based Recurrent Belief State Learning for POMDPs Xiaoyu Chen Yao Mu Ping Luo Sheng Li Jianyu Chen 45 18 0 23 May 2022
Universality of parametric Coupling Flows over parametric diffeomorphisms Junlong Lyu Zhitang Chen Chang Feng Wenjing Cun Shengyu Zhu Yanhui Geng Zhijie Xu Yuxiao Chen 16 3 0 07 Feb 2022
Neural Information Squeezer for Causal Emergence Jiang Zhang Kaiwei Liu CML 30 14 0 25 Jan 2022
Diffeomorphically Learning Stable Koopman Operators Petar Bevanda Maximilian Beier Sebastian Kerz Armin Lederer Stefan Sosnowski Sandra Hirche 37 21 0 08 Dec 2021
Neural Flows: Efficient Alternative to Neural ODEs Marin Bilovs Johanna Sommer Syama Sundar Rangapuram Tim Januschowski Stephan Günnemann AI4TS 33 70 0 25 Oct 2021
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach Petar Bevanda Johannes Kirmayr Stefan Sosnowski Sandra Hirche 38 9 0 15 Oct 2021
Sparse Flows: Pruning Continuous-depth Models Lucas Liebenwein Ramin Hasani Alexander Amini Daniela Rus 26 16 0 24 Jun 2021
Trumpets: Injective Flows for Inference and Inverse Problems K. Kothari AmirEhsan Khorashadizadeh Maarten V. de Hoop Ivan Dokmanić TPM 29 50 0 20 Feb 2021
Jacobian Determinant of Normalizing Flows Huadong Liao Jiawei He DRL 19 7 0 12 Feb 2021
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang Ricky T. Q. Chen Christos Tsirigotis Aaron Courville OT 119 95 0 10 Dec 2020
ChartPointFlow for Topology-Aware 3D Point Cloud Generation Takumi Kimura Takashi Matsubara K. Uehara 3DPC 31 8 0 04 Dec 2020