The rates for representing the class of functionsGD via DReLU layers is sharpuptoconstants,asshownbymatchinglowerbounds.

The subtext to each question is initalics.Theanswers are in plain text with no formatting. Was there a specific gap that needed to be filled?Please provide a description.The Dollar Street dataset is a supervised image dataset derived from Gapminder'sDollar Street project (https://www.gapminder.org/dollar-street)that It was created with three goals in mind:1. Make available a highly curated set of images with valuable metadata (e.g.country, monthly income) that is more closely representative of the geographicand socioeconomic diversity of the world when compared with existing imagedatasets.2. Help combat bias in downstream applications (e.g.

Work todate has highlighted the challenges inmitigating bias within these datasets.

The code is available at https://github.com/ml-ml/Link-MoE/.

This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially. The algorithm allows for the simultaneous training of the model parameters and the distribution of the latent states given the observations. It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility. The performance of the method is illustrated on both synthetic data and real-world air-quality data. The proposed approach is theoretically motivated by the existence of an asymptotic contrast function and the ergodicity of the underlying Markov chain, and applies more generally to the computation of additive expectations under posterior distributions in state-space models.