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.
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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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The relation between crosstalk and gene regulation form revisited
R. Grah, T. Friedlander, PLOS Computational Biology 16 (2020).
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Novel imprints in mouse blastocysts are predominantly DNA methylation independent
L. Santini, F. Halbritter, F. Titz-Teixeira, T. Suzuki, M. Asami, J. Ramesmayer, X. Ma, A. Lackner, N. Warr, F. Pauler, S. Hippenmeyer, E. Laue, M. Farlik, C. Bock, A. Beyer, A.C.F. Perry, M. Leeb, BioRxiv (n.d.).
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Maximizing crosstalk
R. Grah, T. Friedlander, (2020).
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Verifying concurrent programs: Refinement, synchronization, sequentialization
B. Kragl, Verifying Concurrent Programs: Refinement, Synchronization, Sequentialization, Institute of Science and Technology Austria, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8332
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Object-centric learning with slot attention
F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, T. Kipf, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 11525–11538.
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arXiv: 2006.15055

Scaling limits of discrete optimal transport
P. Gladbach, E. Kopfer, J. Maas, SIAM Journal on Mathematical Analysis 52 (2020) 2759–2802.
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arXiv: 1809.01092

Simulating liquids on dynamically warping grids
I. Hikaru, C. Wojtan, N. Thuerey, T. Igarashi, R. Ando, IEEE Transactions on Visualization and Computer Graphics 26 (2020) 2288–2302.
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Rotation of coupled cold molecules in the presence of a many-body environment
X. Li, Rotation of Coupled Cold Molecules in the Presence of a Many-Body Environment, Institute of Science and Technology Austria, 2020.
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Structure-aware computational design and its application to 3D printable volume scattering, mechanism, and multistability
R. Zhang, Structure-Aware Computational Design and Its Application to 3D Printable Volume Scattering, Mechanism, and Multistability, Institute of Science and Technology Austria, 2020.
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Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing
J. Kukucka, Implementation of a Hole Spin Qubit in Ge Hut Wires and Dispersive Spin Sensing, Institute of Science and Technology Austria, 2020.
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Stochastic games with lexicographic reachability-safety objectives
K. Chatterjee, J.P. Katoen, M. Weininger, T. Winkler, in:, International Conference on Computer Aided Verification, Springer Nature, 2020, pp. 398–420.
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arXiv: 2005.04018

Computational analysis of filament polymerization dynamics in cytoskeletal networks
P.R. Dos Santos Caldas, P. Radler, C.M. Sommer, M. Loose, in:, P. Tran (Ed.), Methods in Cell Biology, Elsevier, 2020, pp. 145–161.
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Diffusion and capture permits dynamic coupling between treadmilling FtsZ filaments and cell division proteins
N.S. Baranova, P. Radler, V.M. Hernández-Rocamora, C. Alfonso, M.D. Lopez Pelegrin, G. Rivas, W. Vollmer, M. Loose, Nature Microbiology 5 (2020) 407–417.
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Refutation of a claim made by Fejes Tóth on the accuracy of surface meshes
G. Vegter, M. Wintraecken, Studia Scientiarum Mathematicarum Hungarica 57 (2020) 193–199.
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A note on belief structures and s-approximation spaces
A. Shakiba, A.K. Goharshady, M.R. Hooshmandasl, M. Alambardar Meybodi, Iranian Journal of Mathematical Sciences and Informatics 15 (2020) 117–128.
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arXiv: 1805.10672

On the regularisation of the noise for the Euler-Maruyama scheme with irregular drift
K. Dareiotis, M. Gerencser, Electronic Journal of Probability 25 (2020).
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Leveraging structure in Computer Vision tasks for flexible Deep Learning models
A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models, Institute of Science and Technology Austria, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8390
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