202 citations · 573 across the 58 of their papers we have counts for
4 papers · 1 filter
Intermediate Layers Encode Optimal Biological Representations in Single-Cell Foundation Models
Vincenzo Yuto Civale, Roberto Semeraro, Andrew David Bagdanov +1
Current single-cell foundation model benchmarks universally extract final layer embeddings, assuming these represent optimal feature spaces. We systematically evaluate layer-wise r…
NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons
Carlo Romeo, Andrew D. Bagdanov
Balancing combat encounters in Dungeons & Dragons (D&D) is a complex task that requires Dungeon Masters (DM) to manually assess party strength, enemy composition, and dynamic playe…
RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification
Marco Mistretta, Andrew D. Bagdanov
In this paper we introduce RE-tune, a novel approach for fine-tuning pre-trained Multimodal Biomedical Vision-Language models (VLMs) in Incremental Learning scenarios for multi-lab…
A Benchmark Environment for Offline Reinforcement Learning in Racing Games
Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov
Offline Reinforcement Learning (ORL) is a promising approach to reduce the high sample complexity of traditional Reinforcement Learning (RL) by eliminating the need for continuous…