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8978 Publications


2020 | Preprint | IST-REx-ID: 10022 | OA
D. L. Forkert, J. Maas, and L. Portinale, “Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions,” arXiv. .
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2020 | Conference Paper | IST-REx-ID: 8724 | OA
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
[Published Version] View | Files available | arXiv
 

2020 | Journal Article | IST-REx-ID: 8644 | OA
W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, and G. Bighin, “Detecting composite orders in layered models via machine learning,” New Journal of Physics, vol. 22, no. 9. IOP Publishing, 2020.
[Published Version] View | Files available | DOI | WoS
 

2020 | Journal Article | IST-REx-ID: 8705 | OA
K. Mysliwy and R. Seiringer, “Microscopic derivation of the Fröhlich Hamiltonian for the Bose polaron in the mean-field limit,” Annales Henri Poincare, vol. 21, no. 12. Springer Nature, pp. 4003–4025, 2020.
[Published Version] View | Files available | DOI | WoS | arXiv
 

2020 | Journal Article | IST-REx-ID: 10861
D. Nickovic, O. Lebeltel, O. Maler, T. Ferrere, and D. Ulus, “AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal logic,” International Journal on Software Tools for Technology Transfer, vol. 22, no. 6. Springer Nature, pp. 741–758, 2020.
View | Files available | DOI | WoS
 

2020 | Journal Article | IST-REx-ID: 14125 | OA
Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 36(Supplement_2), i919–i927.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | PubMed | Europe PMC
 

2020 | Conference Paper | IST-REx-ID: 14186 | OA
F. Locatello et al., “A commentary on the unsupervised learning of disentangled representations,” in The 34th AAAI Conference on Artificial Intelligence, New York, NY, United States, 2020, vol. 34, no. 9, pp. 13681–13684.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2020 | Conference Paper | IST-REx-ID: 14188 | OA
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen, “Weakly-supervised disentanglement without compromises,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 6348–6359.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2020 | Conference Paper | IST-REx-ID: 14187 | OA
G. Négiar et al., “Stochastic Frank-Wolfe for constrained finite-sum minimization,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 7253–7262.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2020 | Journal Article | IST-REx-ID: 14195 | OA
F. Locatello et al., “A sober look at the unsupervised learning of disentangled representations and their evaluation,” Journal of Machine Learning Research, vol. 21. MIT Press, 2020.
[Published Version] View | Download Published Version (ext.) | arXiv
 

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