Refinement for structured concurrent programs
B. Kragl, S. Qadeer, T.A. Henzinger, in:, Computer Aided Verification, Springer Nature, 2020, pp. 275–298.
LibreCat: https://research-explorer.ista.ac.at/record/8195
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Inductive sequentialization of asynchronous programs
B. Kragl, C. Enea, T.A. Henzinger, S.O. Mutluergil, S. Qadeer, in:, Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation, Association for Computing Machinery, 2020, pp. 227–242.
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Organization and dynamics of treadmilling filaments in cytoskeletal networks of FtsZ and its crosslinkers
P.R. Dos Santos Caldas, Organization and Dynamics of Treadmilling Filaments in Cytoskeletal Networks of FtsZ and Its Crosslinkers, Institute of Science and Technology Austria, 2020.
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Generalizing CGAL periodic Delaunay triangulations
G.F. Osang, M. Rouxel-Labbé, M. Teillaud, in:, 28th Annual European Symposium on Algorithms, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.
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Functional vs. parametric equivalence of ReLU networks
M. Phuong, C. Lampert, in:, 8th International Conference on Learning Representations, 2020.
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Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball
D. Feliciangeli, R. Seiringer, SIAM Journal on Mathematical Analysis 52 (2020) 605–622.
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Weak–strong uniqueness for the Navier–Stokes equation for two fluids with surface tension
J.L. Fischer, S. Hensel, Archive for Rational Mechanics and Analysis 236 (2020) 967–1087.
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The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions
J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).
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Homogenisation of one-dimensional discrete optimal transport
P. Gladbach, E. Kopfer, J. Maas, L. Portinale, Journal de Mathematiques Pures et Appliquees 139 (2020) 204–234.
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Evolutionary -convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions
D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.).
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arXiv: 2008.10962

On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.
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arXiv: 2002.10384

Detecting composite orders in layered models via machine learning
W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin, New Journal of Physics 22 (2020).
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Microscopic derivation of the Fröhlich Hamiltonian for the Bose polaron in the mean-field limit
K. Mysliwy, R. Seiringer, Annales Henri Poincare 21 (2020) 4003–4025.
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arXiv: 2003.12371

AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal logic
D. Nickovic, O. Lebeltel, O. Maler, T. Ferrere, D. Ulus, International Journal on Software Tools for Technology Transfer 22 (2020) 741–758.
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SCIM: Universal single-cell matching with unpaired feature sets
S.G. Stark, J. Ficek, F. Locatello, X. Bonilla, S. Chevrier, F. Singer, R. Aebersold, F.S. Al-Quaddoomi, J. Albinus, I. Alborelli, S. Andani, P.-O. Attinger, M. Bacac, D. Baumhoer, B. Beck-Schimmer, N. Beerenwinkel, C. Beisel, L. Bernasconi, A. Bertolini, B. Bodenmiller, X. Bonilla, R. Casanova, S. Chevrier, N. Chicherova, M. D’Costa, E. Danenberg, N. Davidson, M.-A.D. gan, R. Dummer, S. Engler, M. Erkens, K. Eschbach, C. Esposito, A. Fedier, P. Ferreira, J. Ficek, A.L. Frei, B. Frey, S. Goetze, L. Grob, G. Gut, D. Günther, M. Haberecker, P. Haeuptle, V. Heinzelmann-Schwarz, S. Herter, R. Holtackers, T. Huesser, A. Irmisch, F. Jacob, A. Jacobs, T.M. Jaeger, K. Jahn, A.R. James, P.M. Jermann, A. Kahles, A. Kahraman, V.H. Koelzer, W. Kuebler, J. Kuipers, C.P. Kunze, C. Kurzeder, K.-V. Lehmann, M. Levesque, S. Lugert, G. Maass, M. Manz, P. Markolin, J. Mena, U. Menzel, J.M. Metzler, N. Miglino, E.S. Milani, H. Moch, S. Muenst, R. Murri, C.K. Ng, S. Nicolet, M. Nowak, P.G. Pedrioli, L. Pelkmans, S. Piscuoglio, M. Prummer, M. Ritter, C. Rommel, M.L. Rosano-González, G. Rätsch, N. Santacroce, J.S. del Castillo, R. Schlenker, P.C. Schwalie, S. Schwan, T. Schär, G. Senti, F. Singer, S. Sivapatham, B. Snijder, B. Sobottka, V.T. Sreedharan, S. Stark, D.J. Stekhoven, A.P. Theocharides, T.M. Thomas, M. Tolnay, V. Tosevski, N.C. Toussaint, M.A. Tuncel, M. Tusup, A.V. Drogen, M. Vetter, T. Vlajnic, S. Weber, W.P. Weber, R. Wegmann, M. Weller, F. Wendt, N. Wey, A. Wicki, B. Wollscheid, S. Yu, J. Ziegler, M. Zimmermann, M. Zoche, G. Zuend, G. Rätsch, K.-V. Lehmann, Bioinformatics 36 (2020) i919–i927.
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PMID: 33381818

A commentary on the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, in:, The 34th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2020, pp. 13681–13684.
LibreCat: https://research-explorer.ista.ac.at/record/14186
DOI: 10.1609/aaai.v34i09.7120
arXiv: 2007.14184

Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, M. Tschannen, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 6348–6359.
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arXiv: 2002.02886

Stochastic Frank-Wolfe for constrained finite-sum minimization
G. Négiar, G. Dresdner, A. Tsai, L.E. Ghaoui, F. Locatello, R.M. Freund, F. Pedregosa, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 7253–7262.
LibreCat: https://research-explorer.ista.ac.at/record/14187
arXiv: 2002.11860

A sober look at the unsupervised learning of disentangled representations and their evaluation
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, Journal of Machine Learning Research 21 (2020).
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arXiv: 2010.14766

The relation between crosstalk and gene regulation form revisited
R. Grah, T. Friedlander, PLOS Computational Biology 16 (2020).
LibreCat: https://research-explorer.ista.ac.at/record/7569
DOI: 10.1371/journal.pcbi.1007642
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