In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
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Question: We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to ''complete the circle,'' where an anatomically grounded whole-connectome simulator is used to impute a time-varying ''brain'' state at single-cell fidelity from covariates that are measurable in practice. Using state of the art Bayesian machine learning methods to condition on readily obtainable data, our method paves the way for neuroscientists to recover interpretable connectome-wide state representations, automatically estimate physiologically relevant parameter values from data, and perform simulations investigating intelligent lifeforms in silico.

Answer: We develop a whole-connectome and body simulator for C. elegans and demonstrate joint state-space and parameter inference in the simulator.

Question: Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works. Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions. Both architectural diversity and routing depth can increase the representational power of a routing network. In this work, we address both of these deficiencies. We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth. In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup. However, when scaling up routing depth, we find that modern routing techniques struggle with optimization. We conclude by discussing both the positive and negative results, and suggest directions for future research.

Answer: Per-example routing models benefit from architectural diversity, but still struggle to scale to a large number of routing decisions.

Question: This paper introduces an information theoretic co-training objective for unsupervised learning. We consider the problem of predicting the future . Rather than predict future sensations (image pixels or sound waves) we predict ''hypotheses'' to be confirmed by future sensations . More formally, we assume a population distribution on pairs $(x,y)$ where we can think of $x$ as a past sensation and $y$ as a future sensation . We train both a predictor model $P_\Phi(z|x)$ and a confirmation model $P_\Psi(z|y)$ where we view $z$ as hypotheses (when predicted) or facts (when confirmed ). For a population distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual information between $x$ and $ y$. By the data processing inequality this mutual information is at least as large as the mutual information between $x$ and $z$ under the distribution on triples $(x,z,y)$ defined by the confirmation model $P_\Psi(z|y)$. The information theoretic training objective for $P_\Phi(z|x)$ and $P_\Psi(z|y)$ can be viewed as a form of co-training where we want the prediction from $x$ to match the confirmation from $y$. We give experiments on applications to learning phonetics on the TIMIT dataset.

Answer:
Presents an information theoretic training objective for co-training and demonstrates its power in unsupervised learning of phonetics.