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Found 3 papers, 1 papers with code
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness
While neural networks can enjoy an outstanding flexibility and exhibit unprecedented performance, the mechanism behind their behavior is still not well-understood.
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
Variational autoencoders (VAEs) are one of the deep generative models that have experienced enormous success over the past decades.
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
The stochastic gradient Langevin Dynamics is one of the most fundamental algorithms to solve sampling problems and non-convex optimization appearing in several machine learning applications.
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