WebFeb 22, 2024 · Abstract. This paper analyzes the minimization of α-divergences in the context of multi-class Gaussian process classification.For this task, several methods are explored, including memory and computationally efficient variants of the Power Expectation Propagation algorithm, which allow for efficient training using stochastic gradients and … WebJan 30, 2024 · The minimization of α-divergences in the context of Bayesian neural networks has also been addressed in . In that work it is described Black-box-α, a …
Black-box -divergence Minimization
WebThis approach is known as black-box VB and it allows VB to be applied to almost any model. Here we propose a similar method for the more general problem of α-divergence minimization and call itblack-box alpha. For this, we simplify the energy function of EP so that the min-maxproblem is transformed into a minimization problem. WebBlack-box alpha divergence minimization. J. Hernandez-Lobato et al. (2016). ICML R´enyi divergence variational inference. Y. Li and R. E Turner (2016). NeurIPS Variational inference via χ-upper bound minimization A. Dieng et al. (2024). NeurIPS Kam´elia Daudel · Monotonic Alpha-divergence Minimisation for Variational Inference7 / 24 is asot1000 going ahead
Black-boxα-divergence Minimization - Approximate Inference
WebJan 30, 2024 · Alpha divergence minimization Before describing the proposed method, we briefly review here the α -divergence, of which we make extensive use. Let p and q be two distributions over the vector θ. The α -divergence between p and q is non-negative and only equal to zero if p = q [19]. WebNov 10, 2015 · Black-box alpha (BB-α) is a new approximate inference method based on the minimization of α-divergences. BB-α scales to large datasets because it can be … http://proceedings.mlr.press/v48/hernandez-lobatob16.html onamia food pantry