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Normalizing flows are explicit likelihood models (ELM) characterized by a flexible invertible reparameterization of high-dimensional probability distributions. Unlike other ELMs, they offer both exact and efficient likelihood computation and data generation. Since their recent introduction, flow-based models have seen a significant resurgence of interest in the machine learning community. As a result, powerful flow-based models have been developed, with successes in density estimation, variational inference, and generative modeling of images, audio and video.
As the field is moving forward, the main goal of the workshop is to consolidate recent progress and connect ideas from related fields. Over the past few years, we’ve seen that normalizing flows are deeply connected to latent variable models, autoregressive models, and more recently, diffusion-based generative models. This year, we would like to further push the forefront of these explicit likelihood models through the lens of invertible reparameterization. We encourage researchers to use these models in conjunction to exploit the their benefits at once, and to work together to resolve some common issues of likelihood-based methods, such as mis-calibration of out-of-distribution uncertainty.
Fri 2:28 a.m. - 2:30 a.m.
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Opening
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Fri 2:30 a.m. - 2:55 a.m.
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Invited Talk 1 (Charline Le Lan): On the use of density models for anomaly detection
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Charline Le Lan 🔗 |
Fri 2:55 a.m. - 3:00 a.m.
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Q&A (Charline Le Lan)
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Fri 3:00 a.m. - 3:25 a.m.
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Invited Talk 2 (Yingzhen Li): Inference with scores: slices, diffusions and flows
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Yingzhen Li 🔗 |
Fri 3:25 a.m. - 3:30 a.m.
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Q&A (Yingzhen Li)
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Fri 3:30 a.m. - 3:35 a.m.
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Spotlight 1: Distilling the Knowledge from Normalizing Flows
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Invertible Workshop INNF 🔗 |
Fri 3:35 a.m. - 3:40 a.m.
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Spotlight 2: Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound
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Invertible Workshop INNF 🔗 |
Fri 3:40 a.m. - 3:45 a.m.
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Spotlight 3: Representational aspects of depth and conditioning in normalizing flows
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Invertible Workshop INNF 🔗 |
Fri 3:45 a.m. - 3:50 a.m.
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Spotlight 4: Rectangular Flows for Manifold Learning
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Invertible Workshop INNF 🔗 |
Fri 3:50 a.m. - 3:55 a.m.
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Spotlight 5: Interpreting diffusion score matching using normalizing flow
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Fri 3:55 a.m. - 4:00 a.m.
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Spotlight 6: Universal Approximation using Well-conditioned Normalizing Flows
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Fri 4:00 a.m. - 5:00 a.m.
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Poster Session 1
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Poster
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Poster room 1: [ protected link dropped ] Poster room 2: [ protected link dropped ] When and Where: https://docs.google.com/spreadsheets/u/1/d/1l1hA6IyEDLkzNMQuO2BLtsLWAI05dEC0R5lxNPJriMY/edit#gid=0 |
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Fri 4:59 a.m. - 5:00 a.m.
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Intro
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Fri 5:00 a.m. - 5:25 a.m.
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Invited Talk 3 (Phiala Shanahan): Flow models for theoretical particle and nuclear physics
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Phiala Shanahan 🔗 |
Fri 5:25 a.m. - 5:30 a.m.
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Q&A (Phiala Shanahan)
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Fri 5:30 a.m. - 5:55 a.m.
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Invited Talk 4 (Marcus Brubaker): Wavelet Flow: Fast Training of High Resolution Normalizing Flows
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Marcus A Brubaker 🔗 |
Fri 5:55 a.m. - 6:00 a.m.
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Q&A (Marcus Brubaker)
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Fri 6:00 a.m. - 7:30 a.m.
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Break
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Fri 7:29 a.m. - 7:30 a.m.
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Intro
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Fri 7:30 a.m. - 7:55 a.m.
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Invited Talk 5 (Stefano Ermon): Maximum Likelihood Training of Score-Based Diffusion Models
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Stefano Ermon 🔗 |
Fri 7:55 a.m. - 8:00 a.m.
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Q&A (Stefano Ermon)
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Fri 8:00 a.m. - 8:25 a.m.
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Contributed Talk 1: Diffeomorphic Explanations with Normalizing Flows
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Fri 8:25 a.m. - 8:30 a.m.
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Q&A (Ann-Kathrin Dombrowski)
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Fri 8:30 a.m. - 8:55 a.m.
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Invited Talk 6 (Maximilian Nickel): Modeling Spatio-Temporal Events via Normalizing Flows
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Maximilian Nickel 🔗 |
Fri 8:55 a.m. - 9:00 a.m.
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Q&A (Maximilian Nickel)
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Fri 9:00 a.m. - 9:25 a.m.
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Invited Talk 7 (Aditya Ramesh): Scaling up generative models
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Fri 9:25 a.m. - 9:30 a.m.
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Q&A (Aditya Ramesh)
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Fri 9:30 a.m. - 9:55 a.m.
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Contributed Talk 2: Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
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Invertible Workshop INNF 🔗 |
Fri 9:55 a.m. - 10:00 a.m.
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Q&A (Marylou Gabrié)
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Fri 10:00 a.m. - 10:05 a.m.
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Spotlight 7: Sliced Iterative Normalizing Flows
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Fri 10:05 a.m. - 10:10 a.m.
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Spotlight 8: Universal Approximation of Residual Flows in Maximum Mean Discrepancy
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Fri 10:10 a.m. - 10:15 a.m.
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Spotlight 9: On Fast Sampling of Diffusion Probabilistic Models
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Fri 10:15 a.m. - 10:20 a.m.
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Spotlight 10: Discrete Denoising Flows
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Fri 10:20 a.m. - 10:25 a.m.
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Spotlight 11: Task-agnostic Continual Learning with Hybrid Probabilistic Models
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Fri 10:25 a.m. - 10:30 a.m.
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Spotlight 12: Conformal Embedding Flows: Tractable Density Estimation on Learned Manifolds
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Invertible Workshop INNF 🔗 |
Fri 10:30 a.m. - 11:30 a.m.
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Poster Session 2
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Poster room 1: [ protected link dropped ] Poster room 2: [ protected link dropped ] When and Where: https://docs.google.com/spreadsheets/u/1/d/1l1hA6IyEDLkzNMQuO2BLtsLWAI05dEC0R5lxNPJriMY/edit#gid=0 |
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Author Information
Chin-Wei Huang (MILA)
David Krueger (Université de Montréal)
Rianne Van den Berg (University of Amsterdam)
George Papamakarios (DeepMind)
Ricky T. Q. Chen (U of Toronto)
Danilo J. Rezende (DeepMind)

Danilo is a Senior Staff Research Scientist at Google DeepMind, where he works on probabilistic machine reasoning and learning algorithms. He has a BA in Physics and MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau – France) and from the Institute of Theoretical Physics (SP – Brazil) and a Ph.D. in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne, EPFL (Lausanne – Switzerland). His research focuses on scalable inference methods, generative models of complex data (such as images and video), applied probability, causal reasoning and unsupervised learning for decision-making.
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