1 citations · 2 across the 3 of their papers we have counts for
6 papers
Hybrid Cold-Start Recommender System for Closure Model Selection in Multiphase Flow Simulations
S. Hänsch, A. Sajdoková, A. RÄbowski +6
Selecting appropriate physical models is a critical yet difficult step in many areas of computational science and engineering. In multiphase Computational Fluid Dynamics (CFD), pra…
Reasoning-Grounded Natural Language Explanations for Language Models
Vojtech Cahlik, Rodrigo Alves, Pavel Kordik
We propose a large language model explainability technique for obtaining faithful natural language explanations by grounding the explanations in a reasoning process. When converted…
Efficient Learning of Sparse Representations from Interactions
VojtÄch VanÄura, Martin Spišák, Rodrigo Alves +1
Behavioral patterns captured in embeddings learned from interaction data are pivotal across various stages of production recommender systems. However, in the initial retrieval stag…
Reflecting in the Reflection: Integrating a Socratic Questioning Framework into Automated AI-Based Question Generation
OndÅej Holub, Essi Ryymin, Rodrigo Alves
Designing good reflection questions is pedagogically important but time-consuming and unevenly supported across teachers. This paper introduces a reflection-in-reflection framework…
From Knots to Knobs: Towards Steerable Collaborative Filtering Using Sparse Autoencoders
Martin Spišák, Ladislav Peška, Petr Škoda +2
Sparse autoencoders (SAEs) have recently emerged as pivotal tools for introspection into large language models. SAEs can uncover high-quality, interpretable features at different l…
The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems
Petr Kasalický, Martin Spišák, VojtÄch VanÄura +3
Industry-scale recommender systems face a core challenge: representing entities with high cardinality, such as users or items, using dense embeddings that must be accessible during…