
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
We perform scalable approximate inference in a recentlyproposed family ...
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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Flowbased models are powerful tools for designing probabilistic models ...
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SelfTuning Stochastic Optimization with CurvatureAware Gradient Filtering
Standard firstorder stochastic optimization algorithms base their updat...
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Neural SpatioTemporal Point Processes
We propose a new class of parameterizations for spatiotemporal point pr...
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Learning Neural Event Functions for Ordinary Differential Equations
The existing Neural ODE formulation relies on an explicit knowledge of t...
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"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code
Neural differential equations may be trained by backpropagating gradient...
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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models p...
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Scalable Gradients for Stochastic Differential Equations
The adjoint sensitivity method scalably computes gradients of solutions ...
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Neural Networks with Cheap Differential Operators
Gradients of neural networks can be computed efficiently for any archite...
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Latent ODEs for IrregularlySampled Time Series
Time series with nonuniform intervals occur in many applications, and a...
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Residual Flows for Invertible Generative Modeling
Flowbased generative models parameterize probability distributions thro...
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FFJORD: Freeform Continuous Dynamics for Scalable Reversible Generative Models
A promising class of generative models maps points from a simple distrib...
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Ricky T. Q. Chen
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