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01/19/2023
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Binary neural networks, i.e., neural networks whose parameters and activ...
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10/13/2022
Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples
Stochastic dynamics are ubiquitous in many fields of science, from the e...
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11/26/2021
Particle Dynamics for Learning EBMs
Energy-based modeling is a promising approach to unsupervised learning, ...
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06/18/2021
Deterministic Gibbs Sampling via Ordinary Differential Equations
Deterministic dynamics is an essential part of many MCMC algorithms, e.g...
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10/15/2020
Orbital MCMC
Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
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06/30/2020
Involutive MCMC: a Unifying Framework
Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
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06/09/2019
The Implicit Metropolis-Hastings Algorithm
Recent works propose using the discriminator of a GAN to filter out unre...
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10/16/2018
Metropolis-Hastings view on variational inference and adversarial training
In this paper we propose to view the acceptance rate of the Metropolis-H...
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03/10/2018
Variance Networks: When Expectation Does Not Meet Your Expectations
In this paper, we propose variance networks, a new model that stores the...
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02/13/2018
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose i...
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05/20/2017