Titlebook: Machine Learning, Optimization, and Data Science; 9th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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古老

Exploring Image Transformations with Diffusion Models: A Survey of Applications and Implementation Cf diffusion models in computer vision, specially in the branch of image transformations. The objective of this survey is to provide an overview of state-of-the-art applications of diffusion models in image transformations, including image inpainting, super-resolution, restoration, translation, and e

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Geolocation Risk Scores for Credit Scoring Modelscluding finance. Commercial banks use this information in the evaluation of their credit scoring systems. Generally, customer city and district are used as demographic features. Even if these features are quite informative, they are not fully capable of capturing socio-economical heterogeneity of cu

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婴儿

A Proximal Algorithm for Network Slimmingh . regularization applied to the scaling factors of the batch normalization layers; (2) it removes channels whose scaling factors are below a chosen threshold; and (3) it retrains the pruned model to recover the original accuracy. This time-consuming, three-step process is a result of using subgrad

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Diversity in Deep Generative Models and Generative AIshow impressive results when constructing objects similar to those in a training ensemble. However, the generation of new objects builds mainly on the understanding of the hidden structure of the training dataset followed by a sampling from a multi-dimensional normal variable. In particular each sam

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Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes ante prediction of regimes, leading to substantial performance improvements over the earlier model, over all three asset classes considered (equities, commodities, and foreign exchange), over a test period of four years. The proposed new model is also benchmarked over this same period against a hid

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Alternating Mixed-Integer Programming and Neural Network Training for Approximating Stochastic Two-Srameters with adjustable decision variables. In case the adjustable or second-stage variables contain discrete decisions, the corresponding 2SPs are known to be .-complete. The standard approach of forming a single-stage deterministic equivalent problem can be computationally challenging even for sm