Correcting nuisance variation using Wasserstein distance [PDF]
PeerJ, 2020 Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.
Gil Tabak +4 more
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Wasserstein Distance-Based Deep Leakage from Gradients [PDF]
Entropy, 2023 Federated learning protects the privacy information in the data set by sharing the average gradient. However, “Deep Leakage from Gradient” (DLG) algorithm as a gradient-based feature reconstruction attack can recover privacy training data using gradients
Zifan Wang +3 more
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Dissimilarity measure of local structure in inorganic crystals using Wasserstein distance to search for novel phosphors [PDF]
Science and Technology of Advanced Materials, 2021 To efficiently search for novel phosphors, we propose a dissimilarity measure of local structure using the Wasserstein distance. This simple and versatile method provides the quantitative dissimilarity of a local structure around a center ion.
Shota Takemura +5 more
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Probability Forecast Combination via Entropy Regularized Wasserstein Distance [PDF]
Entropy, 2020 We propose probability and density forecast combination methods that are defined using the entropy regularized Wasserstein distance. First, we provide a theoretical characterization of the combined density forecast based on the regularized Wasserstein ...
Ryan Cumings-Menon, Minchul Shin
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WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method [PDF]
Sensors, 2021 With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from ...
Zhiyu Zhu +3 more
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An Empirical Study of Self-Supervised Learning with Wasserstein Distance [PDF]
EntropyIn this study, we consider the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where TWD is defined as the L1 distance between two tree-embedded vectors. In SSL
Makoto Yamada +6 more
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Wasserstein Distance Learns Domain Invariant Feature Representations for Drift Compensation of E-Nose [PDF]
Sensors, 2019 Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases.
Yang Tao +4 more
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Identifying critical States of complex diseases by local network Wasserstein distance [PDF]
Scientific ReportsComplex diseases often undergo abrupt transitions from pre-disease to disease states, with the pre-disease state is typically unstable but potentially reversible through timely intervention. Detecting these critical transitions is crucial.
Changchun Liu, Pingjun Hou, Lin Feng
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Quantum Wasserstein distance based on an optimization over separable states [PDF]
Quantum, 2023 We define the quantum Wasserstein distance such that the optimization of the coupling is carried out over bipartite separable states rather than bipartite quantum states in general, and examine its properties. Surprisingly, we find that the self-distance
Géza Tóth, József Pitrik
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Earth mover’s distance as a measure of CP violation
Journal of High Energy Physics, 2023 We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover’s) distance.
Adam Davis +3 more
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