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Algorithmic Robustness for Semi-Supervised (e,g,t)-Good Metric Learning

, , , , and . chapter Algorithmic Robustness for Semi-Supervised (e,g,t)-Good Metric Learning, page 253--263. Springer International Publishing, Cham, (2015)
DOI: 10.1007/978-3-319-26532-2_28

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Joint Semi-supervised Similarity Learning for Linear Classification., , , and . ECML/PKDD (1), volume 9284 of Lecture Notes in Computer Science, page 594-609. Springer, (2015)Extending Knowledge Bases Using Images., , and . AKBC@NIPS, OpenReview.net, (2017)Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning., , , , and . ICLR (Workshop), (2015)Learning similarities for linear classification: theoretical foundations and algorithms. (Apprentissage de similarités pour la classification linéaire: fondements théoriques et algorithmes).. University of Lyon, France, (2016)Algorithmic Robustness for Semi-Supervised (e,g,t)-Good Metric Learning, , , , and . chapter Algorithmic Robustness for Semi-Supervised (e,g,t)-Good Metric Learning, page 253--263. Springer International Publishing, Cham, (2015)Open-World Visual Recognition Using Knowledge Graphs., , and . CoRR, (2017)Similarity Learning for Time Series Classification., , , and . CoRR, (2016)Joint Semi-Supervised Similarity Learning for Linear Classification, , , and . Joint European Conference on Machine Learning and Knowledge Discovery in Databases, page 594--609. Springer, (2015)Adversarial Robustness Toolbox v0.2.2., , , , , , , and . CoRR, (2018)Efficient Defenses Against Adversarial Attacks., , and . AISec@CCS, page 39-49. ACM, (2017)