From the 1 of 11 linked papers with an AI index.
11 papers
CrimeNER Demo: Named-Entity Recognition in the Crime Domain
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
The paper introduces CrimeNER Demo, an AI-driven platform that extracts and classifies crime-related entities from documents using pretrained and user‑fine‑tuned NER models.
Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named…
Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2
Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez +3
We present the Membership Inference Test (MINT) Demo 2, a framework designed to improve transparency in machine learning training processes. MINT is a technique for experimentally…
Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +3
We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP…
Membership Inference Test: Auditing Training Data in Object Classification Models
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +2
In this research, we analyze the performance of Membership Inference Tests (MINT), focusing on determining whether given data were utilized during the training phase, specifically…
Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics
Daniel DeAlcala, Aythami Morales, Julian Fierrez +3
This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifica…