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Philipp Hennig

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Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI

Feb 06, 2024
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Jose Miguel Hernandez Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

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Sample Path Regularity of Gaussian Processes from the Covariance Kernel

Dec 22, 2023
Nathaël Da Costa, Marvin Pförtner, Lancelot Da Costa, Philipp Hennig

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Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures

Nov 01, 2023
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig

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Accelerating Generalized Linear Models by Trading off Computation for Uncertainty

Oct 31, 2023
Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig

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Parallel-in-Time Probabilistic Numerical ODE Solvers

Oct 02, 2023
Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä

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The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions

Jun 28, 2023
Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp

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Benchmarking Neural Network Training Algorithms

Jun 12, 2023
George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson

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Probabilistic Exponential Integrators

May 24, 2023
Nathanael Bosch, Philipp Hennig, Filip Tronarp

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