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Maximilian Nickel
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Latent Discretization for Continuous-time Sequence Compression


Dec 28, 2022
Ricky T. Q. Chen, Matthew Le, Matthew Muckley, Maximilian Nickel, Karen Ullrich

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Flow Matching for Generative Modeling


Oct 06, 2022
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le

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Matching Normalizing Flows and Probability Paths on Manifolds


Jul 11, 2022
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, Yaron Lipman

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* ICML 2022 
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Semi-Discrete Normalizing Flows through Differentiable Tessellation


Mar 14, 2022
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

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Can I see an Example? Active Learning the Long Tail of Attributes and Relations


Mar 11, 2022
Tyler L. Hayes, Maximilian Nickel, Christopher Kanan, Ludovic Denoyer, Arthur Szlam

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Moser Flow: Divergence-based Generative Modeling on Manifolds


Aug 18, 2021
Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman

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Neural Spatio-Temporal Point Processes


Nov 09, 2020
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

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Learning Neural Event Functions for Ordinary Differential Equations


Nov 08, 2020
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

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CURI: A Benchmark for Productive Concept Learning Under Uncertainty


Oct 06, 2020
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden Lake

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Riemannian Continuous Normalizing Flows


Jun 18, 2020
Emile Mathieu, Maximilian Nickel

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