Browsing Universitätsbibliographie by Author "Hennig, Philipp"

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A probabilistic model for the numerical solution of initial value problems

Schober, Michael; Saerkkae, Simo; Hennig, Philipp (2019)
A probabilistic state space model for joint inference from differential equations and data

Schmidt, Jonathan; Krämer, Nicholas; Hennig, Philipp (2021)
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization

De Roos, Filip; Jidling, Carl; Wills, Adrian; Schön, Thomas; Hennig, Philipp (2021)
Accelerating Generalized Linear Models by Trading off Computation for Uncertainty

Hennig, Philipp; Schneider, Frank; Wenger, Jonathan; Tatzel, Lukas (2024)
An Infinite-Feature Extension for Bayesian ReLU nets that fixes their asymptotic overconfidence

Kristiadi, Agustinus; Hein, Matthias; Hennig, Philipp (2020)
Analytical incorporation of fractionation effects in probabilistic treatment planning for intensity-modulated proton therapy

Wahl, Niklas; Hennig, Philipp; Wieser, Hans-Peter; Bangert, Mark (2018)
Analytical probabilistic modeling of dose-volume histograms

Wahl, Niklas; Hennig, Philipp; Wieser, Hans-Peter; Bangert, Mark (2020)
Analytical probabilistic modeling of RBE-weighted dose for ion therapy

Wieser, H. P.; Hennig, P.; Wahl, N.; Bangert, M. (2017)
Analytical probabilistic proton dose calculation and range uncertainties

Bangert, M.; Hennig, P.; Oelfke, U. (2014)
BackPACK: Packing more into Backprop

Dangel, Felix; Kunstner, Frederik; Hennig, Philipp (2020)
Bayesian Numerical Integration with Neural Networks

Hennig, Philipp; Briol, François-Xavier; Tiermann, Michael; Ott, Katharina (2023)
Bayesian ODE solvers: the maximum a posteriori estimate

Tronarp, Filip; Sarkka, Simo; Hennig, Philipp (2021)
Bayesian Quadrature on Riemannian Data Manifolds

Fröhlich, Christian; Gessner, Alexandra; Hennig, Philipp; Schölkopf, Bernhard; Arvanitidis, Georgios (2021)
Baysian numerical integration with neural networks

Briol, François-Xavier; Hennig, Philipp; Tiemann, Michael; Ott, Katharina (2023)
Being a Bit Frequentist Improves Bayesian Neural Networks

Kristiadi, Agustinus; Hein, Matthias; Hennig, Philipp (2021)
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Hein, Matthias; Hennig, Philipp; Kristiadi, Agustinus (2020)
Benchmarking Neural Network Training Algorithms

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

Bosch, Nathanael; Hennig, Philipp; Tronarp, Filip (2021)
Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

Schneider, Frank; Dangel, Felix; Hennig, Philipp (2021)
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference

Cunningham, John P.; Pleiss, Geoff; Gardner, Jacob R.; Hennig, Philipp; Wu, Kaiwen; Wenger, Jonathan (2024)

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