---
title: "Who cites ENIGMA-NG: Efficient Neural and — 14 citing papers"
description: "14 papers of the Elamur Research library cite «ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ENIGMA-NG: Efficient Neural and Gradient-Boosted…](https://md.research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/) / cited by

# Who cites «ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E»

14 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E»](https://md.research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/)

1. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/)
2. **[ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)](https://md.research.elamur.ai/free/en/papers/jakubuv-2020-enigma-anonymous-symbol-independent-inference/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Jan Jakubův, Karel Chvalovský, Miroslav Olšák, et al.*2020 · 42 citations*
3. **[First Neural Conjecturing Datasets and Experiments](https://md.research.elamur.ai/free/en/papers/urban-2020-first-neural-conjecturing-datasets-experiments/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Josef Urban, Jan Jakubův*2020 · 40 citations*
4. **[Property Invariant Embedding for Automated Reasoning](https://md.research.elamur.ai/free/en/papers/olsak-2019-property-invariant-embedding-automated-reasoning/) [Frontiers in artificial intelligence and applications](https://md.research.elamur.ai/free/en/venues/frontiers-in-artificial-intelligence-and-applications/)**Miroslav Olšák, Cezary Kaliszyk, Josef Urban*2019 · 30 citations*
5. **[Towards Finding Longer Proofs](https://md.research.elamur.ai/free/en/papers/zombori-2019-finding-longer-proofs/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, et al.*2019 · 21 citations*
6. [**Graph2Tac: Online Representation Learning of Formal Math Concepts arXiv:2401.02949**Lasse Blaauwbroek, Miroslav Olšák, Jason Rute, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/blaauwbroek-2024-graph2tac-online-representation-learning-formal/)
7. **[Deep Reinforcement Learning for Synthesizing Functions in Higher-Order Logic](https://md.research.elamur.ai/free/en/papers/gauthier-2019-deep-reinforcement-learning-synthesizing-functions/) [EPiC series in computing](https://md.research.elamur.ai/free/en/venues/epic-series-in-computing/)**Thibault Gauthier*2019 · 15 citations*
8. **[Guiding Inferences in Connection Tableau by Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/piotrowski-2019-guiding-inferences-connection-tableau-recurrent/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Bartosz Piotrowski, Josef Urban*2019 · 15 citations*

6 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/) — ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E