We prove that the ELBO is bounded, and the EM algorithm contributes to the convergence of ELBO.

This is an idealization of the usual way to train neural networks with a large hidden layer.

This paper concerns the structure of learned representations in text-guided generative models, focusing on score-based models.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Recent progress in the development of voltage indicators [1-8] has brought us closer to a longstanding goal incellular neuroscience: imaging the full spatiotemporal voltageonadendritic tree. These recordings have the potential (pun not intended) to resolve fundamental questions about the computations performed by dendrites -- questions that have remained open for more than a century[9,10].

We consider the problem of minimizing a continuous function given given access to a natural quantum generalization of a stochastic gradient oracle. We provide two new methods for the special case of minimizing a Lipschitz convex function. Each method obtains a dimension versus accuracy trade-off which is provably unachievable classically and we prove that one method is asymptotically optimal in low-dimensional settings. Additionally, we provide quantum algorithms for computing a critical point of a smooth non-convex function at rates not known to be achievable classically. To obtain these results we build upon the quantum multivariate mean estimation result of Cornelissen et al. [25] and provide a general quantum variance reduction technique of independent interest.

Neural Information Processing Systems http://nips.cc/