# Actively Learning what makes a Discrete Sequence Valid

arXiv: https://arxiv.org/abs/1708.04465 · HTML: https://mlsanity.com/paper/1708.04465/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2017.

## Built on by (most cited first, 5 of 5)

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- [Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient](https://mlsanity.com/paper/2405.18075/) (2024)
- [Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing](https://mlsanity.com/paper/2112.13737/) (2021)

## Abstract (arXiv)

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.