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.

[EX Q]: In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to storage, communication, or privacy constraints. In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global satisficing (i.e. "good-enough") model within a few communication rounds by carefully combining the space of local nodes' satisficing models. In experiments on benchmark and medical datasets, our approach outperforms other baseline aggregation techniques such as ensembling or model averaging, and performs comparably to the ideal non-distributed models.

[EX A]: We present Good-Enough Model Spaces (GEMS), a framework for learning an aggregate model over distributed nodes within a small number of communication rounds.

[EX Q]: As deep learning-based classifiers are increasingly adopted in real-world applications, the importance of understanding how a particular label is chosen grows. Single decision trees are an example of a simple, interpretable classifier, but are unsuitable for use with complex, high-dimensional data. On the other hand, the variational autoencoder (VAE) is designed to learn a factored, low-dimensional representation of data, but typically encodes high-likelihood data in an intrinsically non-separable way. We introduce the differentiable decision tree (DDT) as a modular component of deep networks and a simple, differentiable loss function that allows for end-to-end optimization of a deep network to compress high-dimensional data for classification by a single decision tree. We also explore the power of labeled data in a supervised VAE (SVAE) with a Gaussian mixture prior, which leverages label information to produce a high-quality generative model with improved bounds on log-likelihood. We combine the SVAE with the DDT to get our classifier+VAE (C+VAE), which is competitive in both classification error and log-likelihood, despite optimizing both simultaneously and using a very simple encoder/decoder architecture.
[EX A]: We combine differentiable decision trees with supervised variational autoencoders to enhance interpretability of classification.

[EX Q]: The problem of visual metamerism is defined as finding a family of perceptually
indistinguishable, yet physically different images. In this paper, we propose our
NeuroFovea metamer model, a foveated generative model that is based on a mixture
of peripheral representations and style transfer forward-pass algorithms. Our
gradient-descent free model is parametrized by a foveated VGG19 encoder-decoder
which allows us to encode images in high dimensional space and interpolate
between the content and texture information with adaptive instance normalization
anywhere in the visual field. Our contributions include: 1) A framework for
computing metamers that resembles a noisy communication system via a foveated
feed-forward encoder-decoder network We observe that metamerism arises as a
byproduct of noisy perturbations that partially lie in the perceptual null space; 2)
A perceptual optimization scheme as a solution to the hyperparametric nature of
our metamer model that requires tuning of the image-texture tradeoff coefficients
everywhere in the visual field which are a consequence of internal noise; 3) An
ABX psychophysical evaluation of our metamers where we also find that the rate
of growth of the receptive fields in our model match V1 for reference metamers
and V2 between synthesized samples. Our model also renders metamers at roughly
a second, presenting a 1000 speed-up compared to the previous work, which now
allows for tractable data-driven metamer experiments.
[EX A]:
We introduce a novel feed-forward framework to generate visual metamers