# Mesters'eges Intelligencia Kutat'asok Magyarorsz'agon

arXiv: https://arxiv.org/abs/2503.05767 · HTML: https://mlsanity.com/paper/2503.05767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 113 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 16 of 16)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Gradient Descent Provably Optimizes Over-parameterized Neural Networks](https://mlsanity.com/paper/1810.02054/) (2018)
- [MMD GAN: Towards Deeper Understanding of Moment Matching Network](https://mlsanity.com/paper/1705.08584/) (2017)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Similarity and Matching of Neural Network Representations](https://mlsanity.com/paper/2110.14633/) (2021)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [Sign-Perturbed Sums: A New System Identification Approach for Constructing Exact Non-Asymptotic Confidence Regions in Linear Regression Models](https://mlsanity.com/paper/1807.08216/) (2018)
- [Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users](https://mlsanity.com/paper/2005.14051/) (2020)
- [Targeted Adversarial Attacks on Generalizable Neural Radiance Fields](https://mlsanity.com/paper/2310.03578/) (2023)
- [Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations](https://mlsanity.com/paper/1812.09632/) (2018)
- [Morphosyntactic probing of multilingual BERT models](https://mlsanity.com/paper/2306.06205/) (2023)
- [Theoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis](https://mlsanity.com/paper/2310.09961/) (2023)
- [Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning](https://mlsanity.com/paper/2209.14652/) (2022)
- [Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off](https://mlsanity.com/paper/2407.09150/) (2024)

## Abstract (arXiv)

Artificial intelligence (AI) has undergone remarkable development since the mid-2000s, particularly in the fields of machine learning and deep learning, driven by the explosive growth of large databases and computational capacity. Hungarian researchers recognized the significance of AI early on, actively participating in international research and achieving significant results in both theoretical and practical domains. This article presents some key achievements in Hungarian AI research. It highlights the results from the period before the rise of deep learning (the early 2010s), then discusses major theoretical advancements in Hungary after 2010. Finally, it provides a brief overview of AI-related applied scientific achievements from 2010 onward.