analysis to understand tools in Bayesian deep learning. The title should be on the top of the poster and use large fonts, as this is what will be shown to attendees as they approach your poster, see the screenshot here. Foong, Alexander Amini, Wilko Schwarting, Ava Soleimany, Danela Rus, Ava P. Soleimany, alexander amini, Samuel Goldman, Daniela Rus, Sangeeta N. Bhatia, Connor W. Coley, Javier Antoran, James Urquhart Allingham, Jose Miguel Hernandez-Lobato, Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram, Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai, Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker, Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling, Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk, Takuo Matsubara, Christ Oates, Francois-Xavier Briol, Vincent Fortuin, Adria Garriga-Alonso, Florian Wenzel, Gunnar Ratsch, Richard Turner, Mark van der Wilk, Lawrence Aitchison, Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, Jose Miguel Hernandez-Lobato, Daniel Barrejon-Moreno, Pablo M. Olmos, Antonio Artes-Rodriguez, Marcin B. Tomczak, Siddharth Swaroop, Richard E. Turner, Andrey Malinin, Sergey Chervontsev, Ivan Provilkov, Bruno Mlodozeniec, Mark Gales, Audrey Flower, Beliz Gokkaya, Sahar Karimi, Jessica Ai, Ousmane Dia, Ehsan Emamjomeh-Zadeh, Ilknur Kaynar Kabul, Erik Meijer, Adly Templeton, Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Bin Dong, Xinyu Zhou, Sebastian G. Popescu, David J. Sharp, James H. Cole, and Ben Glocker, Austin Tripp, Erik Daxberger, Jose Miguel Hernandez-Lobato, Lisha Chen, Hanjing Wang, Shiyu Chang, Hui Su, Qiang Ji, Jongseok Lee, Matthias Humt, Jianxing Feng, Rudolph Triebel, Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan, Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal, Alexander Lyzhov, Daria Voronkova, Dmitry Vetrov, Moritz Fuchs, Simon Kiefhaber, Hendrik Mehrtens, Faraz Zaidi, Camila Gonzalez, Arjan Kuijper, Anirban Mukhopadhyay, Gautham Krishna Gudur, Abhijith Ragav, Prahalathan Sundaramoorthy, Venkatesh Umaashankar, Ivan Kiskin, Adam D. Cobb, Steve Roberts, Janis Postels, Hermann Blum, Cesar Cadena, Roland Siegwart, Luc van Gool, Federico Tombari, Kristian Miok, Blaz Skrlj, Daniela Zaharie, Marko Robnik-Sikonja, Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka, Lassi Meronen, Martin Trapp, Arno Solin, Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal, Bobby He, Balaji Lakshminarayanan, Yee Whye Teh, Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane, Luca Bortolussi, Guido Sanguinetti, X. Liu, K. Ye, H.W.T van Vlijmen, M.T.M. Representation learning: a review and new perspectives. Incorporating explicit prior knowledge in deep learning (such as posterior regularisation with logic rules). While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. Speech recognition, image recognition, finding patterns in a dataset, object classification in photographs, character text generation, self-driving cars and many more are just a few examples. Datasets also suffer from “dataset bias,” which happens when the training data is not representative of the future deployment domain. Epub 2018 Sep 22. eCollection 2020 Dec. Sci Rep. 2021 Jan 15;11(1):1519. doi: 10.1038/s41598-020-80300-6. Stock trading strategies play a critical role in investment. Mol Pharm. In this skilltest, we tested our community on basic concepts of Deep Learning. The theory of reinforcement learning provides a normative account deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. Please see instructions below. Posters will be uploaded to this website after the event. 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