Residual Flows for Invertible Generative Modeling

6 June 2019

Papers citing "Residual Flows for Invertible Generative Modeling"

50 / 106 papers shown

Title
Why Calibration Error is Wrong Given Model Uncertainty: Using Posterior Predictive Checks with Deep Learning Achintya Gopal UQCV 30 1 0 02 Dec 2021
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 18 101 0 30 Nov 2021
Characteristic Neural Ordinary Differential Equations Xingzi Xu Ali Hasan Khalil Elkhalil Jie Ding Vahid Tarokh BDL 34 3 0 25 Nov 2021
Generalized Normalizing Flows via Markov Chains Paul Hagemann J. Hertrich Gabriele Steidl BDL DiffM AI4CE 30 22 0 24 Nov 2021
On Training Implicit Models Zhengyang Geng Xinyu Zhang Shaojie Bai Yisen Wang Zhouchen Lin 72 69 0 09 Nov 2021
Resampling Base Distributions of Normalizing Flows Vincent Stimper Bernhard Schölkopf José Miguel Hernández-Lobato BDL 30 32 0 29 Oct 2021
Diffusion Normalizing Flow Qinsheng Zhang Yongxin Chen DiffM 35 87 0 14 Oct 2021
Deep Generative Modeling for Protein Design Alexey Strokach Philip M. Kim AI4CE 179 90 0 31 Aug 2021
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training Hari Prasanna Das Ryan Tran Japjot Singh Yu-Wen Lin C. Spanos OOD 21 11 0 25 Aug 2021
Moser Flow: Divergence-based Generative Modeling on Manifolds N. Rozen Aditya Grover Maximilian Nickel Y. Lipman DRL AI4CE 27 57 0 18 Aug 2021
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation Nikolaos Dionelis Mehrdad Yaghoobi Sotirios A. Tsaftaris GAN 25 14 0 24 Jul 2021
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary Nikolaos Dionelis Mehrdad Yaghoobi Sotirios A. Tsaftaris 18 12 0 21 Jul 2021
Copula-Based Normalizing Flows M. Laszkiewicz Johannes Lederer Asja Fischer 36 7 0 15 Jul 2021
Task-agnostic Continual Learning with Hybrid Probabilistic Models Polina Kirichenko Mehrdad Farajtabar Dushyant Rao Balaji Lakshminarayanan Nir Levine Ang Li Huiyi Hu A. Wilson Razvan Pascanu VLM BDL CLL 27 19 0 24 Jun 2021
Sparse Flows: Pruning Continuous-depth Models Lucas Liebenwein Ramin Hasani Alexander Amini Daniela Rus 26 16 0 24 Jun 2021
Approximation capabilities of measure-preserving neural networks Aiqing Zhu Pengzhan Jin Yifa Tang 34 8 0 21 Jun 2021
Independent mechanism analysis, a new concept? Luigi Gresele Julius von Kügelgen Vincent Stimper Bernhard Schölkopf M. Besserve CML 23 100 0 09 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 40 53 0 01 Jun 2021
Efficient and Accurate Gradients for Neural SDEs Patrick Kidger James Foster Xuechen Li Terry Lyons DiffM 29 61 0 27 May 2021
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery Weibing Zhao Xu Yan Jiantao Gao Ruimao Zhang Jiayan Zhang Zhen Li Song Wu Shuguang Cui 3DPC 18 7 0 30 Apr 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 48 485 0 08 Mar 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
Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski Luhuan Wu D. Biderman Geoff Pleiss John P. Cunningham 26 19 0 12 Feb 2021
Jacobian Determinant of Normalizing Flows Huadong Liao Jiawei He DRL 19 7 0 12 Feb 2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs Juntang Zhuang Nicha Dvornek S. Tatikonda James S. Duncan 27 49 0 09 Feb 2021
Variational Determinant Estimation with Spherical Normalizing Flows Simon Passenheim Emiel Hoogeboom BDL 31 1 0 24 Dec 2020
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 96 0 10 Dec 2020
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 96 6,126 0 26 Nov 2020
Self Normalizing Flows Thomas Anderson Keller Jorn W. T. Peters P. Jaini Emiel Hoogeboom Patrick Forré Max Welling 30 14 0 14 Nov 2020
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference M. Vandegar Michael Kagan Antoine Wehenkel Gilles Louppe 34 27 0 11 Nov 2020
Principled Interpolation in Normalizing Flows Samuel G. Fadel Sebastian Mair Ricardo da S. Torres Ulf Brefeld 83 3 0 22 Oct 2020
Self-Supervised Variational Auto-Encoders Ioannis Gatopoulos Jakub M. Tomczak 37 13 0 05 Oct 2020
Discrete Point Flow Networks for Efficient Point Cloud Generation Roman Klokov Edmond Boyer Jakob Verbeek 3DPC 31 107 0 20 Jul 2020
Deep composition of tensor-trains using squared inverse Rosenblatt transports Tiangang Cui S. Dolgov OT 28 33 0 14 Jul 2020
Riemannian Continuous Normalizing Flows Emile Mathieu Maximilian Nickel AI4CE 27 119 0 18 Jun 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal Daniel Sheldon Justin Domke TPM BDL 19 38 0 18 Jun 2020
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks Jens Behrmann Paul Vicol Kuan-Chieh Jackson Wang Roger C. Grosse J. Jacobsen 23 93 0 16 Jun 2020
Learning normalizing flows from Entropy-Kantorovich potentials Chris Finlay Augusto Gerolin Adam M. Oberman Aram-Alexandre Pooladian 33 23 0 10 Jun 2020
The Lipschitz Constant of Self-Attention Hyunjik Kim George Papamakarios A. Mnih 16 135 0 08 Jun 2020
The Convolution Exponential and Generalized Sylvester Flows Emiel Hoogeboom Victor Garcia Satorras Jakub M. Tomczak Max Welling 30 28 0 02 Jun 2020
Invertible Image Rescaling Mingqing Xiao Shuxin Zheng Chang-Shu Liu Yaolong Wang Di He Guolin Ke Jiang Bian Zhouchen Lin Tie-Yan Liu SupR 33 234 0 12 May 2020
Hybrid Models for Open Set Recognition Hongjie Zhang Ang Li Jie Guo Yanwen Guo BDL 28 184 0 27 Mar 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models Chin-Wei Huang Laurent Dinh Aaron Courville DRL 31 87 0 17 Feb 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 57 176 0 16 Feb 2020
How to train your neural ODE: the world of Jacobian and kinetic regularization Chris Finlay J. Jacobsen L. Nurbekyan Adam M. Oberman 11 296 0 07 Feb 2020
Learning Discrete Distributions by Dequantization Emiel Hoogeboom Taco S. Cohen Jakub M. Tomczak DRL 34 31 0 30 Jan 2020
Invertible Generative Modeling using Linear Rational Splines H. M. Dolatabadi S. Erfani C. Leckie 40 65 0 15 Jan 2020
Semi-Supervised Learning with Normalizing Flows Pavel Izmailov Polina Kirichenko Marc Finzi A. Wilson DRL BDL 40 111 0 30 Dec 2019
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One Will Grathwohl Kuan-Chieh Jackson Wang J. Jacobsen David Duvenaud Mohammad Norouzi Kevin Swersky VLM 43 529 0 06 Dec 2019
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 67 1,635 0 05 Dec 2019