5 citations · 10 across the 4 of their papers we have counts for
5 papers
Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles
Roman Neruda, Martin Bakoš, Josef Šlerka +3
Large language models (LLMs) trained on large-scale internet corpora encode extensive statistical regularities about social identities, attitudes, and political behaviour. This pap…
Utilizing Novelty-based Evolution Strategies to Train Transformers in Reinforcement Learning
Matyáš Lorenc, Roman Neruda
In this paper, we experiment with novelty-based variants of OpenAI-ES, the NS-ES and NSR-ES algorithms, and evaluate their effectiveness in training complex, transformer-based arch…
Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning
Matyáš Lorenc, Roman Neruda
We explore the capability of evolution strategies to train an agent with a policy based on a transformer architecture in a reinforcement learning setting. We performed experiments…
Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes
Josef Moudřík, Roman Neruda
The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base lear…
Evaluating Go Game Records for Prediction of Player Attributes
Josef Moudřík, Petr Baudiš, Roman Neruda
We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or…