Results 71 to 80 of about 23,924 (195)
Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
Naval Research Logistics (NRL), EarlyView.ABSTRACT The rapid expansion of cross‐border e‐commerce (CBEC) has created significant opportunities for small‐ and medium‐sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third‐party logistics (3PL)‐led supply chain finance (SCF) has emerged as a promising solution, leveraging in‐transit inventory as ...
Qingkai Zhang, L. Jeff Hong, Houmin Yan
wiley +1 more source
IEEE AccessFederated Learning (FL) enhances privacy but remains vulnerable to model poisoning attacks, where an adversary manipulates client models to upload poisoned updates during training, thereby compromising the overall FL model.
Suzan Almutairi, Ahmed Barnawi
doaj +1 more source
Distributional Reinforcement Learning with Quantile Regression
, 2017 In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term
Bellemare, Marc G. +3 more
core +1 more source
People and Nature, EarlyView.Abstract High aggregate levels of wildlife consumption in cities in Central Africa highlight the need for solutions that balance wildlife protection, local livelihoods and the relational values between people and nature. This study explores the impacts of demand‐ and supply‐side interventions on wild meat consumption through two randomized control ...
Abdoulaye Cisse +2 more
wiley +1 more source
A View on Optimal Transport from Noncommutative Geometry
Symmetry, Integrability and Geometry: Methods and Applications, 2010 We discuss the relation between the Wasserstein distance of order 1 between probability distributions on a metric space, arising in the study of Monge-Kantorovich transport problem, and the spectral distance of noncommutative geometry.
Francesco D'Andrea, Pierre Martinetti
doaj +1 more source
Learning with a Wasserstein loss [PDF]
, 2017 Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions.In this paper we develop a loss function for multi-label learning, based on the Wasserstein ...
Araya-Polo, Mauricio +4 more
core
Next‐generation sequencing in newborn screening: Current status, challenges, and future perspectives
Pediatric Investigation, EarlyView.Newborn screening shifts from biochemical to genetic methods. Global exploration is promising but requires overcoming challenges and international collaboration to optimize implementation. ABSTRACT Newborn screening (NBS) is a key public health intervention that improves children's health outcomes by enabling precise intervention through the early ...
Zhelan Huang, Wenhao Zhou
wiley +1 more source
Bayes and maximum likelihood for $L^1$-Wasserstein deconvolution of Laplace mixtures
, 2017 We consider the problem of recovering a distribution function on the real line from observations additively contaminated with errors following the standard Laplace distribution.
Scricciolo, Catia
core +1 more source
{Euclidean, metric, and Wasserstein} gradient flows: an overview
Bulletin of Mathematical Sciences, 2017 This is an expository paper on the theory of gradient flows, and in particular of those PDEs which can be interpreted as gradient flows for the Wasserstein metric on the space of probability measures (a distance induced by optimal transport). The starting point is the Euclidean theory, and then its generalization to metric spaces, according to the work
openaire +2 more sources