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11357 Publications


2021 | Published | Conference Paper | IST-REx-ID: 14176 | OA
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in Proceedings of 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 11964–11974.
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2021 | Published | Conference Paper | IST-REx-ID: 14177 | OA
F. Träuble et al., “On disentangled representations learned from correlated data,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 10401–10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2021 | Published | Conference Paper | IST-REx-ID: 14178 | OA
A. Dittadi et al., “On the transfer of disentangled representations in realistic settings,” in The Ninth International Conference on Learning Representations, Virtual, 2021.
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2021 | Published | Conference Paper | IST-REx-ID: 14179 | OA
J. von Kügelgen et al., “Self-supervised learning with data augmentations provably isolates content from style,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2021 | Published | Conference Paper | IST-REx-ID: 14180 | OA
N. Rahaman et al., “Dynamic inference with neural interpreters,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 10985–10998.
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2021 | Published | Conference Paper | IST-REx-ID: 14181 | OA
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 

2021 | Published | Conference Paper | IST-REx-ID: 14182 | OA
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2021 | Patent | IST-REx-ID: 14185 | OA
D. Weissenborn et al., “Object-centric learning with slot attention.” 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2021 | Submitted | Preprint | IST-REx-ID: 14221 | OA
F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2021 | Published | Conference Paper | IST-REx-ID: 14332
F. Träuble et al., “Representation learning for out-of-distribution generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised Reinforcement Learning, Virtual, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 17876
T. Fu, K. Frommer, C. Nuckolls, and L. Venkataraman, “Single-molecule junction formation in break-junction measurements,” The Journal of Physical Chemistry Letters, vol. 12, no. 44. American Chemical Society, pp. 10802–10807, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 17877
I. Stone et al., “A single-molecule blueprint for synthesis,” Nature Reviews Chemistry, vol. 5, no. 10. Springer Nature, pp. 695–710, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 17899 | OA
B. Zhang, M. H. Garner, L. Li, L. M. Campos, G. C. Solomon, and L. Venkataraman, “Destructive quantum interference in heterocyclic alkanes: The search for ultra-short molecular insulators,” Chemical Science, vol. 12, no. 30. Royal Society of Chemistry, pp. 10299–10305, 2021.
[Published Version] View | DOI | Download Published Version (ext.) | PubMed | Europe PMC
 

2021 | Published | Journal Article | IST-REx-ID: 17900
J. E. Greenwald et al., “Highly nonlinear transport across single-molecule junctions via destructive quantum interference,” Nature Nanotechnology, vol. 16, no. 3. Springer Nature, pp. 313–317, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 17901
S. Medina Rivero et al., “Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene,” Chemical Communications, vol. 57, no. 5. Royal Society of Chemistry, pp. 591–594, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 18192 | OA
A. Bohrdt et al., “Analyzing nonequilibrium quantum states through snapshots with artificial neural networks,” Physical Review Letters, vol. 127, no. 15. American Physical Society, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 18193 | OA
F. A. Palm, M. Buser, J. Leonard, M. Aidelsburger, U. Schollwöck, and F. Grusdt, “Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model,” Physical Review B, vol. 103, no. 16. American Physical Society, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 18233 | OA
Y. Nahshan et al., “Loss aware post-training quantization,” Machine Learning, vol. 110, no. 11–12. Springer Nature, pp. 3245–3262, 2021.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2021 | Published | Journal Article | IST-REx-ID: 18234
C. Baskin et al., “NICE: Noise Injection and Clamping Estimation for neural network quantization,” Mathematics, vol. 9, no. 17. MDPI, 2021.
[Published Version] View | DOI | arXiv
 

2021 | Published | Journal Article | IST-REx-ID: 18235 | OA
S. Doveh et al., “MetAdapt: Meta-learned task-adaptive architecture for few-shot classification,” Pattern Recognition Letters, vol. 149. Elsevier, pp. 130–136, 2021.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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