v1v2 (latest)

The Convolution Exponential and Generalized Sylvester Flows

Neural Information Processing Systems (NeurIPS), 2020

2 June 2020

Emiel Hoogeboom

Victor Garcia Satorras

Jakub M. Tomczak

Max Welling

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Papers citing "The Convolution Exponential and Generalized Sylvester Flows"

22 / 22 papers shown

Title
Unitary convolutions for learning on graphs and groupsNeural Information Processing Systems (NeurIPS), 2024 B. Kiani Lukas Fesser Melanie Weber GNN 162 3 0 07 Oct 2024
Gradients of Functions of Large Matrices Nicholas Krämer Pablo Moreno-Muñoz Hrittik Roy Søren Hauberg 134 2 0 27 May 2024
1-Lipschitz Layers Compared: Memory, Speed, and Certifiable RobustnessComputer Vision and Pattern Recognition (CVPR), 2023 Bernd Prach Fabio Brau Giorgio Buttazzo Christoph H. Lampert 148 11 0 28 Nov 2023
A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology P. Van-Lane J. Speagle S. Douglas 67 2 0 17 Jul 2023
Geometric Clifford Algebra NetworksInternational Conference on Machine Learning (ICML), 2023 David Ruhe Jayesh K. Gupta Steven De Keninck Max Welling Johannes Brandstetter AI4CE 250 49 0 13 Feb 2023
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style TransferInternational Conference on Language Resources and Evaluation (LREC), 2022 Kangchen Zhu Zhiliang Tian Ruifeng Luo Xiaoguang Mao OOD 161 6 0 19 Dec 2022
Improved techniques for deterministic l2 robustnessNeural Information Processing Systems (NeurIPS), 2022 Sahil Singla Soheil Feizi AAML 148 11 0 15 Nov 2022
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study David Ruhe Kaze W. K. Wong M. Cranmer Patrick Forré 200 8 0 15 Nov 2022
Invertible Monotone Operators for Normalizing FlowsNeural Information Processing Systems (NeurIPS), 2022 Byeongkeun Ahn Chiyoon Kim Youngjoon Hong Hyunwoo J. Kim TPM 146 8 0 15 Oct 2022
ButterflyFlow: Building Invertible Layers with Butterfly MatricesInternational Conference on Machine Learning (ICML), 2022 Chenlin Meng Linqi Zhou Kristy Choi Tri Dao Stefano Ermon TPM 242 13 0 28 Sep 2022
Flowification: Everything is a Normalizing FlowNeural Information Processing Systems (NeurIPS), 2022 Bálint Máté Samuel Klein J. A. Raine Franccois Fleuret 154 6 0 30 May 2022
projUNN: efficient method for training deep networks with unitary matricesNeural Information Processing Systems (NeurIPS), 2022 B. Kiani Randall Balestriero Yann LeCun S. Lloyd 320 34 0 10 Mar 2022
Equivariant Finite Normalizing Flows A. Bose Marcus A. Brubaker I. Kobyzev DRL 164 11 0 16 Oct 2021
Multi-Resolution Continuous Normalizing Flows Vikram S. Voleti Chris Finlay Adam M. Oberman Christopher Pal 192 5 0 15 Jun 2021
Skew Orthogonal ConvolutionsInternational Conference on Machine Learning (ICML), 2021 Sahil Singla Soheil Feizi 124 72 0 24 May 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021 Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 529 597 0 08 Mar 2021
Invertible DenseNets with Concatenated LipSwishNeural Information Processing Systems (NeurIPS), 2021 Yura Perugachi-Diaz Jakub M. Tomczak Sandjai Bhulai 234 23 0 04 Feb 2021
General Invertible Transformations for Flow-based Generative Modeling Jakub M. Tomczak DRL AI4CE 118 5 0 30 Nov 2020
Self Normalizing FlowsInternational Conference on Machine Learning (ICML), 2020 Thomas Anderson Keller Jorn W. T. Peters P. Jaini Emiel Hoogeboom Patrick Forré Max Welling 140 14 0 14 Nov 2020
Training Invertible Linear Layers through Rank-One Perturbations Andreas Krämer Jonas Köhler Frank Noé 162 0 0 14 Oct 2020
Self-Supervised Variational Auto-Encoders Ioannis Gatopoulos Jakub M. Tomczak 86 13 0 05 Oct 2020
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows Didrik Nielsen P. Jaini Emiel Hoogeboom Ole Winther Max Welling TPM BDL DRL 179 95 0 06 Jul 2020