6 papers
Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models
Lotfi Abdelkrim Mecharbat, Abdelrahman Elsayed, Ibrahim Almakky +2
Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning specializes a VFM without shri…
CopyShield: A Cross-Level Benchmark of Copyright Defenses in LLMs
Maryam Alshehyari, Dushyant Singh Chauhan, Samuele Poppi +3
Large language models can reproduce memorized text verbatim, yet copyright defenses are usually evaluated under incompatible protocols. We introduce CopyShield, a controlled benchm…
Score-Only Distillation for Compact Dense Retrieval
Kirill Dubovikov, Martin Takac, Salem Lahlou
Large embedding models improve retrieval quality, but serving large encoders online is expensive. We study whether a compact retriever can learn teacher ranking behavior from score…
PETRA: Transforming Web Text for Petroleum-Engineering Domain Adaptation
Kirill Dubovikov, Omar El Mansouri, Hachem Madmoun +12
Petroleum-engineering search exposes a supervision gap for strong general retrievers: relevant evidence exists in public web text, but domain relevance labels are scarce. To addres…
Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages
Jozef Kubík, Marek Šuppa, Martin Takáč
Limited data for low-resource languages typically yield weaker language models (LMs). Since pre-training is compute-intensive, it is more pragmatic to target improvements during fi…
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search
Lotfi Abdelkrim Mecharbat, Ibrahim Almakky, Martin Takac +1
Deep learning (DL) has achieved remarkable progress in the field of medical imaging. However, adapting DL models to medical tasks remains a significant challenge, primarily due to…