4 papers
Understanding Adversarial Training with Energy-based Models
Mujtaba Hussain Mirza, Maria Rosaria Briglia, Filippo Bartolucci +3
We aim at using Energy-based Model (EBM) framework to better understand adversarial training (AT) in classifiers, and additionally to analyze the intrinsic generative capabilities…
A System for Comprehensive Assessment of RAG Frameworks
Mattia Rengo, Senad Beadini, Domenico Alfano +1
Retrieval Augmented Generation (RAG) has emerged as a standard paradigm for enhancing the factual accuracy and contextual relevance of Large Language Models (LLMs) by integrating r…
Shedding More Light on Robust Classifiers under the lens of Energy-based Models
Mujtaba Hussain Mirza, Maria Rosaria Briglia, Senad Beadini +1
By reinterpreting a robust discriminative classifier as Energy-based Model (EBM), we offer a new take on the dynamics of adversarial training (AT). Our analysis of the energy lands…
Exploring the Connection between Robust and Generative Models
Senad Beadini, Iacopo Masi
We offer a study that connects robust discriminative classifiers trained with adversarial training (AT) with generative modeling in the form of Energy-based Models (EBM). We do so…