Tootfinder

Call for Participation: Help ACLS Understand Public Scholarship on Religion
https://ift.tt/x3fjbIW
The Publicly Engaged Religion Mapping Project invites scholars, practitioners, and community…
via Input 4 RELCFP

Explaining Models under Multivariate Bernoulli Distribution via Hoeffding Decomposition
Baptiste Ferrere (EDF R\&D PRISME, IMT, SINCLAIR AI Lab), Nicolas Bousquet (EDF R\&D PRISME, SINCLAIR AI Lab, LPSM), Fabrice Gamboa (IMT), Jean-Michel Loubes (IMT), Joseph Mur\'e (EDF R\&D PRISME)
https://arxiv.org/abs/2510.07088

Explaining Models under Multivariate Bernoulli Distribution via Hoeffding Decomposition
Explaining the behavior of predictive models with random inputs can be achieved through sub-models decomposition, where such sub-models have easier interpretable features. Arising from the uncertainty quantification community, recent results have demonstrated the existence and uniqueness of a generalized Hoeffding decomposition for such predictive models when the stochastic input variables are correlated, based on concepts of oblique projection onto L 2 subspaces. This article focuses on the ca…

Sparse deepfake detection promotes better disentanglement
Antoine Teissier, Marie Tahon, Nicolas Dugu\'e, Aghilas Sini
https://arxiv.org/abs/2510.05696 https://

Sparse deepfake detection promotes better disentanglement
Due to the rapid progress of speech synthesis, deepfake detection has become a major concern in the speech processing community. Because it is a critical task, systems must not only be efficient and robust, but also provide interpretable explanations. Among the different approaches for explainability, we focus on the interpretation of latent representations. In such paper, we focus on the last layer of embeddings of AASIST, a deepfake detection architecture. We use a TopK activation inspired by…

Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs
Theo Koraag, Niklas Wagner, Felix Dobslaw, Lucas Gren
https://arxiv.org/abs/2509.24344

Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs
Context: Large Language Models (LLMs) enable automation of complex natural language processing across domains, but research on domain-specific applications like Finance remains limited. Objectives: This study explored open-source and commercial LLMs for financial report analysis and commentary generation, focusing on software engineering challenges in implementation. Methods: Using Design Science Research methodology, an exploratory case study iteratively designed and evaluated two LLM-based sy…