Frank Schneider

According to our database, Frank Schneider authored at least 8 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning.

Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig

CoRR, 2024

2023

Accelerating Generalized Linear Models by Trading off Computation for Uncertainty.

Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig

CoRR, 2023

Benchmarking Neural Network Training Algorithms.

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

CoRR, 2023

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Understanding Deep Learning Optimization via Benchmarking and Debugging.

Frank Schneider

PhD thesis, 2022

2021

Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks.

Frank Schneider, Felix Dangel, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers.

Robin M. Schmidt, Frank Schneider, Philipp Hennig

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

DeepOBS: A Deep Learning Optimizer Benchmark Suite.

Frank Schneider, Lukas Balles, Philipp Hennig

Proceedings of the 7th International Conference on Learning Representations, 2019