CybAI news

arXiv:2411.14511v1 Announce Type: new Abstract: We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently estimate complex posterior distributions arising from stochastic simulations. We explore two variations of this approach distinguished by their treatment of the prior distribution. The first model adapts the prior based on observed data using a multivariate prior network, enhancing generalization across various posterior queries. In contrast, the second model utilizes a standard Gaussian prior, offering simplicity while still effectively capturing complex posterior distributions. We demonstrate the efficacy of these…

arXiv:2411.14517v1 Announce Type: new Abstract: Contrastive Language-Image Pre-Training (CLIP) is highly instrumental in machine learning applications within a large variety of domains. We investigate the geometry of this embedding, which is still not well understood. We examine the raw unnormalized embedding and show that text and image reside on linearly separable ellipsoid shells, not centered at the origin. We explain the benefits of having this structure, allowing to better embed instances according to their uncertainty during contrastive training. Frequent concepts in the dataset yield more false negatives, inducing greater uncertainty. A new notion of conformity is introduced, which measures the…

arXiv:2411.14463v1 Announce Type: new Abstract: This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential within the banking sector. This research addresses this specific gap by providing a systematic review and strategic analysis of AI and NLP applications in bank marketing, focusing on their integration across the customer journey and operational excellence. Employing the PRISMA methodology, this…

[Submitted on 22 Oct 2024 (v1), last revised 22 Nov 2024 (this version, v2)] View a PDF of the paper titled Just In Time Transformers, by Ahmed Ala Eddine Benali and 4 other authors View PDF HTML (experimental) Abstract:Precise energy load forecasting in residential households is crucial for mitigating carbon emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility companies and policymakers, who advocate sustainable energy practices, to optimize resource utilization. Moreover, smart meters provide valuable information by allowing for granular insights into consumption patterns. Building upon available smart meter data, our study aims to cluster consumers into distinct…

[Submitted on 5 Apr 2024 (v1), last revised 21 Nov 2024 (this version, v3)] View a PDF of the paper titled Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity, by Zitao Shuai and 3 other authors View PDF HTML (experimental) Abstract:Vision-language pre-training (VLP) has emerged as an effective scheme for multimodal representation learning, but its reliance on large-scale multimodal data poses significant challenges for medical applications. Federated learning (FL) offers a promising solution to scale up the dataset for medical VLP while preserving data privacy. However, we observe that client data heterogeneity in real-world scenarios could cause…

arXiv:2411.15122v1 Announce Type: cross Abstract: AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset consisting of 10,000 studies, and three public datasets (MIMIC-CXR, IU-Xray, CheXpert Plus) for report generation assessment. ReXrank employs 8 evaluation metrics and separately assesses models capable of generating only findings sections and those providing both findings and impressions sections. By providing this standardized evaluation…

[Submitted on 7 Jul 2023 (v1), last revised 21 Nov 2024 (this version, v4)] View a PDF of the paper titled How to use model architecture and training environment to estimate the energy consumption of DL training, by Santiago del Rey and 3 other authors View PDF HTML (experimental) Abstract:To raise awareness of the huge impact Deep Learning (DL) has on the environment, several works have tried to estimate the energy consumption and carbon footprint of DL-based systems across their life cycle. However, the estimations for energy consumption in the training stage usually rely on assumptions that have not been…