Workshop on Invertible Neural Nets and Normalizing Flows

INNF 2019 Accepted Papers

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1. Block Neural Autoregressive Flow
   
   Nicola De Cao, Wilker Aziz and Ivan Titov.
2. Sum-of-Squares Polynomial Flows
   
   Priyank Jaini, Kira Selby and Yaoliang Yu.
3. Inverting Deep Generative models, One layer at a time
   
   Qi Lei, Ajil Jalal, Inderjit Dhillon and Alexandros Dimakis.
4. Normalizing flows for novelty detection in industrial time series data
   
   Maximilian Schmidt and Marko Simic.
5. Embarassingly Parallel MCMC using Real NVP
   
   Diego Mesquita, Paul Blomstedt and Samuel Kaski.
6. Benchmarking Invertible Architectures on Inverse Problems
   
   Jakob Kruse, Lynton Ardizzone and Ullrich Köthe.
7. Residual Flows: Unbiased Generative Modeling with Norm-Learned i-ResNets
   
   Ricky T. Q. Chen, Jens Behrmann and Jörn Jacobsen.
8. PRECOG: PREdictions Conditioned On Goals in Visual Multi-Agent Scenarios
   
   Nicholas Rhinehart, Rowan McAllister, Kris Kitani and Sergey Levine.
9. Improving Exploration in Soft-Actor-Critic with Normalizing Flows Policies
   
   Patrick Nadeem Ward, Ariella Smofsky and Avishek Joey Bose.
10. JacNet: Learning Functions with Structured Jacobian
    
    Safwan Hossain and Jonathan Lorraine.
11. Boosting Trust Region Policy Optimization with Normalizing Flows Policy
    
    Yunhao Tang and Shipra Agrawal.
12. Optimal Domain Translation
    
    Emmanuel de Bézenac, Ibrahim Ayed and Patrick Gallinari.
13. Structured Output Learning with Conditional Generative Flows
    
    You Lu and Bert Huang.
14. Cubic-Spline Flows
    
    Conor Durkan, Artur Bekasov, Iain Murray and George Papamakarios.
15. Information Theory in Density Destructors
    
    J. Emmanuel Johnson, Valero Laparra, Raul Santos-Rodriguez, Gustau Camps-Valls and Jesus Malo.
16. Adversarial training of partially invertible variational autoencoders
    
    Thomas Lucas, Konstantin Shmelkov, Kartheek Alahari, Cordelia Schmid and Jakob Verbeek.
17. VideoFlow: A Flow-Based Generative Model for Video
    
    Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh and Durk Kingma.
18. Monte Carlo Integration with Normalizing Flows
    
    Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross and Jan Novák.
19. Semi-Conditional Normalizing Flows for Semi-Supervised Learning
    
    Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik and Dmitry Vetrov.
20. Symmetric Convolutional Flow
    
    Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Daniel Duckworth and Laurent Dinh.
21. Approximating exponential family models (not single distributions) with a two-network architecture
    
    Sean Bittner and John Cunningham.
22. Covering up bias with Markov blankets: A post-hoc cure for attribute prior blindness
    
    Vinay Prabhu, Dian Ang Yap and Alexandar Wang.
23. Neural Networks with Cheap Differential Operators
    
    Ricky T. Q. Chen and David Duvenaud.
24. AlignFlow: Learning from multiple domains via normalizing flows
    
    Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao and Stefano Ermon.
25. Invertible ConvNets
    
    Marc Finzi, Pavel Izmailov, Wesley Maddox, Polina Kirichenko and Andrew Wilson.
26. On Mixed Conditional FFJORD with Large-Batch Training
    
    Tan Nguyen, Animesh Garg, Anjul Patney, Richard Baraniuk and Anima Anandkumar.
27. Semi-Supervised Learning with Normalizing Flows
    
    Pavel Izmailov, Polina Kirichenko, Marc Finzi and Andrew Wilson.
28. Investigating the Impact of Normalizing Flows on Latent Variable Machine Translation
    
    Michael Przystupa, Mark Schmidt and Muhammad Abdul-Mageed.
29. Improving Normalizing Flows via Better Orthogonal Parameterizations
    
    Adam Golinski, Mario Lezcano-Casado and Tom Rainforth.
30. MinvNet: Building Invertible Neural Networks with Masked Convolutions
    
    Yang Song, Chenlin Meng and Stefano Ermon.
31. Learning Generative Samplers using Relaxed Injective Flow
    
    Abhishek Kumar, Ben Poole and Kevin Murphy.