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Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators

20 June 2020

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

26 / 76 papers shown

Title
Diffeomorphically Learning Stable Koopman Operators Petar Bevanda Maximilian Beier Sebastian Kerz Armin Lederer Stefan Sosnowski Sandra Hirche 73 21 0 08 Dec 2021
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression Akifumi Okuno Masaaki Imaizumi 52 1 0 01 Dec 2021
Neural Flows: Efficient Alternative to Neural ODEs Marin Bilovs Johanna Sommer Syama Sundar Rangapuram Tim Januschowski Stephan Günnemann AI4TS 80 77 0 25 Oct 2021
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach Petar Bevanda Johannes Kirmayr Stefan Sosnowski Sandra Hirche 114 9 0 15 Oct 2021
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows Michael Puthawala Matti Lassas Ivan Dokmanić Maarten V. de Hoop 102 13 0 08 Oct 2021
Sparse approximation of triangular transports. Part II: the infinite dimensional case Jakob Zech Youssef Marzouk 83 19 0 28 Jul 2021
On the expressivity of bi-Lipschitz normalizing flows Alexandre Verine Benjamin Négrevergne F. Rossi Y. Chevaleyre TPM 94 17 0 15 Jul 2021
Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows Holden Lee Chirag Pabbaraju A. Sevekari Andrej Risteski 74 8 0 07 Jul 2021
Learning ODEs via Diffeomorphisms for Fast and Robust Integration Weiming Zhi Tin Lai Lionel Ott Edwin V. Bonilla Fabio Ramos OOD 42 4 0 04 Jul 2021
On the Generative Utility of Cyclic Conditionals Chang-Shu Liu Haoyue Tang Tao Qin Jintao Wang Tie-Yan Liu 82 3 0 30 Jun 2021
Sparse Flows: Pruning Continuous-depth Models Lucas Liebenwein Ramin Hasani Alexander Amini Daniela Rus 116 17 0 24 Jun 2021
EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models Qi Ma S. Ghosh 70 5 0 09 Jun 2021
Universal Approximation of Residual Flows in Maximum Mean Discrepancy Zhifeng Kong Kamalika Chaudhuri UQCV 42 6 0 10 Mar 2021
Memory-Efficient Network for Large-scale Video Compressive Sensing Ziheng Cheng Bo Chen Guanliang Liu Hao Zhang Ruiying Lu Zhengjue Wang Xin Yuan 93 63 0 04 Mar 2021
Abelian Neural Networks Kenshi Abe Takanori Maehara Issei Sato 45 2 0 24 Feb 2021
KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss Yu-Hsueh Chen Takashi Matsubara Takaharu Yaguchi 36 4 0 22 Feb 2021
Trumpets: Injective Flows for Inference and Inverse Problems K. Kothari AmirEhsan Khorashadizadeh Maarten V. de Hoop Ivan Dokmanić TPM 56 50 0 20 Feb 2021
Jacobian Determinant of Normalizing Flows Huadong Liao Jiawei He DRL 47 8 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 230 98 0 10 Dec 2020
Universal Approximation Property of Neural Ordinary Differential Equations Takeshi Teshima Koichi Tojo Masahiro Ikeda Isao Ishikawa Kenta Oono 94 40 0 04 Dec 2020
ChartPointFlow for Topology-Aware 3D Point Cloud Generation Takumi Kimura Takashi Matsubara K. Uehara 3DPC 101 8 0 04 Dec 2020
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning H. Asatryan Hanno Gottschalk Marieke Lippert Matthias Rottmann GAN 143 10 0 24 Nov 2020
Representational aspects of depth and conditioning in normalizing flows Frederic Koehler Viraj Mehta Andrej Risteski TPM 55 24 0 02 Oct 2020
On the representation and learning of monotone triangular transport maps Ricardo Baptista Youssef Marzouk O. Zahm 103 49 0 22 Sep 2020
Learning Dynamics Models with Stable Invariant Sets Naoya Takeishi Yoshinobu Kawahara 62 18 0 16 Jun 2020
Structure preserving deep learning E. Celledoni Matthias Joachim Ehrhardt Christian Etmann R. McLachlan B. Owren Carola-Bibiane Schönlieb Ferdia Sherry AI4CE 122 44 0 05 Jun 2020