133 citations · 159 across the 3 of their papers we have counts for
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Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen +2
We present a new method for evaluating and training unnormalized density models. Our approach only requires access to the gradient of the unnormalized model's log-density. We estim…
How to train your neural ODE: the world of Jacobian and kinetic regularization
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan +1
Training neural ODEs on large datasets has not been tractable due to the necessity of allowing the adaptive numerical ODE solver to refine its step size to very small values. In pr…
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud +1
Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks prov…