Results 121 to 130 of about 32,144 (182)
Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance. [PDF]
Entropy (Basel), 2023 He X, Peng C, Wang L, Tan W, Wang Z.
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Methods in Ecology and Evolution, Volume 17, Issue 2, Page 301-321, February 2026.Abstract Quantifying the structure and dynamics of species interactions in ecological communities is fundamental to studying ecology and evolution. While there are numerous approaches to analysing ecological networks, there is not yet an approach that can (1) quantify dissimilarity in the global structure of ecological networks that range from ...
Kai M. Hung +4 more
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Optimal 1-Wasserstein distance for WGANs
BernoulliThe mathematical forces at work behind Generative Adversarial Networks raise challenging theoretical issues. Motivated by the important question of characterizing the geometrical properties of the generated distributions, we provide a thorough analysis of Wasserstein GANs (WGANs) in both the finite sample and asymptotic regimes.
Stéphanovitch, Arthur +4 more
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Leveraging machine learning and accelerometry to classify animal behaviours with uncertainty
Methods in Ecology and Evolution, Volume 17, Issue 2, Page 567-584, February 2026.Abstract Animal‐worn sensors have revolutionised the study of animal behaviour and ecology. Accelerometers, which measure changes in acceleration across planes of movement, are increasingly being used in conjunction with machine learning models to classify animal behaviours across taxa and research questions.
Medha Agarwal +4 more
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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
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Effect sizes for experimental research
British Journal of Mathematical and Statistical Psychology, Volume 79, Issue 1, Page 31-45, February 2026.Abstract Good scientific practice requires that the reporting of the statistical analysis of experiments should include estimates of effect size as well as the results of tests of statistical significance. Good statistical practice requires that effect size estimates be reported along with some indication of their statistical uncertainty, such as a ...
Larry V. Hedges
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, 2017 The Wasserstein distance between two probability measures on a metric space is a measure of closeness with applications in statistics, probability, and machine learning.
Bach, Francis, Weed, Jonathan
core
Global Change Biology, Volume 32, Issue 2, February 2026.Transforming recorded sound into meaningful ecological insights requires carefully designed methodologies. This workflow diagram describes the steps of our analysis (contained within circles labeled 1–7), which builds on best practices to correct for potential biases.
Giacomo L. Delgado +15 more
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Towards Analysis of Covariance Descriptors via Bures–Wasserstein Distance
MathematicsA brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices, enabling users to control them through thought.
Huajun Huang +4 more
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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. [PDF]
BMC Biol, 2023 Zhu H, Hao H, Yu L.
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