1 citations · 2 across the 6 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. Rębowski +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…
Efficient Learning of Sparse Representations from Interactions
Vojtěch Vančura, 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
Ondřej 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, Vojtěch Vančura +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…
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…