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.

Q: As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role.   To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes.   We propose a comparative approach, nearest neighbor overlap (N2O), that quantifies similarity between embedders in a task-agnostic manner.   N2O requires only a collection of examples and is simple to understand: two embedders are more similar if, for the same set of inputs, there is greater overlap between the inputs' nearest neighbors.   We use N2O to compare 21 sentence embedders and show the effects of different design choices and architectures.

A: We propose nearest neighbor overlap, a procedure which quantifies similarity between embedders in a task-agnostic manner, and use it to compare 21 sentence embedders.
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Q: A framework for efficient Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation. Its strength lies in both ease of implementation and automatically tuning sampler parameters to speed up mixing time. Several strategies to approximate the evidence lower bound (ELBO) computation are introduced, including a rewriting of the ELBO objective. Experimental evidence is shown by performing experiments on an unconditional VAE on density estimation tasks; solving an influence diagram in a high-dimensional space with a conditional variational autoencoder (cVAE) as a deep Bayes classifier; and state-space models for time-series data.

A: We embed SG-MCMC samplers inside a variational approximation
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Q: We present a tool for Interactive Visual Exploration of Latent Space (IVELS) for model selection.   Evaluating generative models of discrete sequences from a  continuous  latent  space  is  a  challenging  problem,  since  their  optimization involves multiple competing objective terms.   We introduce a model-selection pipeline  to  compare  and  filter  models  throughout  consecutive  stages  of  more complex and expensive metrics. We present the pipeline in an interactive visual tool to enable the exploration of the metrics, analysis of the learned latent space, and selection of the best model for a given task.   We focus specifically on the variational auto-encoder family in a case study of modeling peptide sequences, which are short sequences of amino acids. This task is especially interesting due to the presence of multiple attributes we want to model. We demonstrate how an interactive visual comparison can assist in evaluating how well an unsupervised auto-encoder meaningfully captures the attributes of interest in its latent space.

A:
We present a visual tool to interactively explore the latent space of an auto-encoder for peptide sequences and their attributes.
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