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Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case.
The exit from a metastable state: concentration of the exit point distribution on the low energy saddle points, part 2
We consider the first exit point distribution from a bounded domain $\Omega$ of the stochastic process $(X_t)_{t\ge 0}$ solution to the overdamped Langevin dynamics $$d X_t = -\nabla f(X_t) d t + \sqrt{h} \ d B_t$$ starting from deterministic initial conditions in $\Omega$, under rather general assumptions on $f$ (for instance, $f$ may have several critical points in $\Omega$).
Analysis of PDEs Mathematical Physics Mathematical Physics Probability
