You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Deep learning techniques have been hugely successful for traditional supervised and unsupervised machine learning problems. In large part, these techniques solve continuous optimization problems. Recently however, discrete generative deep learning models have been successfully used to efficiently search high-dimensional discrete spaces. These methods work by representing discrete objects as sequences, for which powerful sequence-based deep models can be employed. Unfortunately, these techniques are significantly hindered by the fact that these generative models often produce invalid sequences. As a step towards solving this problem, we propose to learn a deep recurrent validator model. Given a partial sequence, our model learns the probability of that sequence occurring as the beginning of a full valid sequence. Thus this identifies valid versus invalid sequences and crucially it also provides insight about how individual sequence elements influence the validity of discrete objects. To learn this model we propose an approach inspired by seminal work in Bayesian active learning. On a synthetic dataset, we demonstrate the ability of our model to distinguish valid and invalid sequences. We believe this is a key step toward learning generative models that faithfully produce valid discrete objects.

Cited Abstract(s):
[1]: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion
Current methods for attacking these discrete search problems work by lifting' the search from discrete space to continuous space, via an [1] . Specifically, an autoencoder jointly learns two mappings: 1) a mapping from discrete space to continuous space called an ; and 2) a reverse mapping from continuous space back to discrete space called a . These mappings are learned so that if we map a discrete object to a continuous one via the encoder, then map it back via the decoder, we reconstruct the original object. The hope is that, once the autoencoder is fully trained, the continuous space (often called the latent' space) acts as proxy for the discrete space. If this holds, we can use the geometry of the continuous space to improve search using (Euclidean) distance measures and gradients, among many other things. showed that is possible to use this technique to search for promising drug molecules.