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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Lidar Upsampling With Sliced Wasserstein Distance [PDF]
IEEE Robotics and Automation Letters, 2023 Lidar became an important component of the perception systems in autonomous driving. But challenges of training data acquisition and annotation made emphasized the role of the sensor to sensor domain adaptation.
A. Savkin +4 more
semanticscholar +3 more sources
Fused Gromov-Wasserstein Distance for Structured Objects [PDF]
Algorithms, 2020 Optimal transport theory has recently found many applications in machine learning thanks to its capacity to meaningfully compare various machine learning objects that are viewed as distributions.
Titouan Vayer +4 more
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Federated Wasserstein Distance [PDF]
International Conference on Learning Representations, 2023 We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner. Namely, we show how to estimate the Wasserstein distance between two samples stored and kept on different devices/clients whilst a ...
Alain Rakotomamonjy +2 more
semanticscholar +3 more sources
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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Wasserstein Distance Guided Representation Learning for Domain Adaptation
AAAI Conference on Artificial Intelligence, 2018 Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution.
Qu, Yanru +3 more
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Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance [PDF]
Bernoulli, 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
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Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
Applied Sciences, 2023 Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world
Wei Zhang +4 more
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