Our work on how LLMs store relations selected as NeurIPS Spotlight paper

Our paper The Structure of Relation Decoding Linear Operators in Large Language Models by Miranda Anna Christ, Adrián Csiszárik, Gergely Becsó, and Dániel Varga was accepted at the NeurIPS 2025 conference as a Spotlight paper (~3% of submissions).

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Healthcare

Prevention & Prediction

Patient Pathways

Patient Pathway Mission

Leveraging Hungary’s healthcare data assets for prevention, prediction, and decision support.

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The table

All Blog posts Publications Events Grants Announcements

Training BERT Models to Carry Over a Coding System Developed on One Corpus to Another

Publication

February 22, 2024

publications

This paper describes how we train BERT models to carry over a coding system developed on the paragraphs of a Hungarian literary journal to another. The aim of the coding system is to track trends in the perception of literary translation around the political transformation in 1989 in Hungary. To evaluate not only task performance but also the consistence of the annotation, moreover, to get better predictions from an ensemble, we use 10-fold crossvalidation.

February 22, 2024

Önfelügyelt reprezentációtanulás komplex adatokon (in hungarian)

Event

December 6, 2023

events

A reprezentációtanulás, vagyis adatokból magasabb szintű ismeret, „tudás” kinyerése a mesterséges intelligencia kutatásának egy kiemelt kérdése. Új, tudományos és társadalmi hasznosulás szempontjából is jelentős terület az önfelügyelt reprezentáció tanulás, amely a felügyelt tanuláshoz képest jóval nagyobb léptékű adatbázisokon, széles körben alkalmazható nagy modellek tanítását teszi lehetővé. Nagy nyelvi modellek tanításának mára alapeleme az önfelügyelt tanítás, és gépi látásban is több sikeres megközelítést publikáltak. Kevesebb kutatás irányul azonban arra, hogy a vizuális önfelügyelt előtanítással kialakított hálók összetett gépi látási feladatokon (pl.

December 6, 2023

Mode Combinability: Exploring Convex Combinations of Permutation Aligned Models (in hungarian)

Event

November 22, 2023

events

Mode Combinability: Exploring Convex Combinations of Permutation Aligned Models Adrián Csiszárik, Melinda F. Kiss, Péter Kőrösi-Szabó, Márton Muntag, Gergely Papp, Dániel Varga As recently discovered (Ainsworth-Hayase-Srinivasa 2022 and others), two wide neural networks with identical network topology and trained on similar data can be permutation-aligned. That is, we can shuffle their neurons (channels) so that linearly interpolating between the two networks in parameter space becomes a meaningful operation (linear mode connectivity).

November 22, 2023

Targeted Adversarial Attacks on Generalizable Neural Radiance Fields

Event

November 15, 2023

events

Contemporary robotics relies heavily on addressing key challenges like odometry, localization, depth perception, semantic segmentation, the creation of new viewpoints, and navigation with precision and efficiency. Implicit neural representation techniques, notably Neural Radiance Fields (NeRFs) and Generalizable NeRFs (GeNeRFs), are increasingly employed to tackle these issues. This talk focuses on exposing certain critical, but subtle flaws inherent in GeNeRFs. Adversarial attacks, while not new to various machine learning frameworks, present a significant threat.

November 15, 2023

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The Team

The AI group at the institute brings together experts with backgrounds in both industry and academia. We place equal emphasis on theoretical foundations, thorough experimentation, and practical applications. Our close collaboration ensures a continuous exchange of knowledge between scientific research and applied projects.

All Theory Applications Former members

Balázs Szegedy

Mathematical Theory

Attila Börcs, PhD

NLP, Modeling, MLOps

Adrián Csiszárik

Representation Learning, Foundations

Győző Csóka

NLP, MLOps

Domonkos Czifra

NLP, Foundations

Botond Forrai

Modeling

Péter Kőrösi-Szabó

Modeling

Gábor Kovács

NLP, Modeling

Judit Laki, MD PhD

Healthcare

Márton Muntag

Time Series, NLP, Modeling

Dávid Terjék

Generalization, Mathematical Theory

Dániel Varga

Foundations, Computer aided proofs

Pál Zsámboki

Reinforcement Learning, Geometric Deep Learning

Zsolt Zombori

Formal Reasoning

Péter Ágoston

Combinatory, Geometry

Beatrix Mária Benkő

Representation Learning

Jakab Buda

NLP

Diego González Sánchez

Generalization, Mathematical Theory

Melinda F. Kiss

Representation Learning

Ákos Matszangosz

Topology, Foundations

Alex Olár

Foundations

Gergely Papp

Modeling

Open Positions

The Rényi AI group is actively recruiting both theorists and practitioners.

Machine Learning Engineer

Announcement: July 1, 2025

Deadline: rolling

applications

Rényi Institute is seeking Machine Learning Engineers to join our AI Research & Development team. Preferred Qualifications: • MLOps experience (especially in cloud environments) • Industry experience working on ML solutions

Research Scientist

Announcement: July 1, 2025

Deadline: rolling

theory, applications

Rényi Institute is seeking Research Scientists to join our AI Research & Development team. You will have the privilege to work at a renowned academic institute and do what you love: do research and publish in the field of machine learning / deep learning.

All Positions

Rényi AI - Building bridges between mathematics and artificial intelligence.