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## Schedule for upcoming Meetings
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tbd
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### 15 February 2021 Explainable Machine Learning
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* Unmasking Clever Hans predictors and assessing what machines really learn<br>
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Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller <br>
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Nat Commun 10, 1096 (2019)<br>
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https://doi.org/10.1038/s41467-019-08987-4<br>
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6 pages
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* Discovering physical concepts with neural networks<br>
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Raban Iten, Tony Metger, Henrik Wilming, Lidia del Rio, Renato Renner <br>
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Physical Review Letters, 124(1), 2020, 010508<br>
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https://arxiv.org/abs/1807.10300<br>
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5 pages + 11 pages Appendix :)
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### 15 March 2021 Model uncertainty
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?<br>
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Alex Kendall, Yarin Gal<br>
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NIPS2017<br>
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https://www.semanticscholar.org/paper/What-Uncertainties-Do-We-Need-in-Bayesian-Deep-for-Kendall-Gal/ff7bcaa4556cb13fc7bf03e477172493546172cd <br>
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10 pages
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* Weight Uncertainty in Neural Networks<br>
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Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra<br>
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ICML 2015<br>
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https://arxiv.org/abs/1505.05424<br>
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8 pages
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### 19 April 2021 Model uncertainty
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* A Simple Baseline for Bayesian Uncertainty in Deep Learning<br>
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Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson<br>
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Neurisp 2019<br>
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https://proceedings.neurips.cc/paper/2019/file/118921efba23fc329e6560b27861f0c2-Paper.pdf<br>
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9 pages
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* A General Framework for Uncertainty Estimation in Deep Learning, IEEE Robotics and Automation Letters
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A Loquercio, M Segù, D. Scaramuzza, 2020,
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https://arxiv.org/pdf/1907.06890.pdf
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8 pages
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## Past Meetings
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