Frank Schneider

19 papers A* 4Journal 13Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition. CoRR Priya Kasimbeg, Frank Schneider, Runa Eschenhagen, Juhan Bae, Chandramouli Shama Sastry, Mark Saroufim, Boyuan Feng, Less Wright, Edward Z. Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, George E. Dahl
2025	J	`jnl`	Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty. Trans. Mach. Learn. Res. Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig
2025	A*	`conf`	Accelerating neural network training: An analysis of the AlgoPerf competition. ICLR Priya Kasimbeg, Frank Schneider, Runa Eschenhagen, Juhan Bae, Chandramouli Shama Sastry, Mark Saroufim, Boyuan Feng, Less Wright, Edward Z. Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, George E. Dahl
2025	J	`jnl`	Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods. CoRR Andres Fernandez, Frank Schneider, Maren Mahsereci, Philipp Hennig
2025	J	`jnl`	Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods. Trans. Mach. Learn. Res. Andres Fernandez, Frank Schneider, Maren Mahsereci, Philipp Hennig
2025	J	`jnl`	Mitigating Forgetting in Low Rank Adaptation. CoRR Joanna Sliwa, Frank Schneider, Philipp Hennig, José Miguel Hernández-Lobato
2025	J	`jnl`	Sketching Low-Rank Plus Diagonal Matrices. CoRR Andres Fernandez, Felix Dangel, Philipp Hennig, Frank Schneider
2024	J	`jnl`	Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning. CoRR Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
2023	J	`jnl`	Accelerating Generalized Linear Models by Trading off Computation for Uncertainty. CoRR Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig
2023	J	`jnl`	Benchmarking Neural Network Training Algorithms. CoRR 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
2023	A*	`conf`	Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
2023	J	`jnl`	Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
2022	—	`—`	Understanding Deep Learning Optimization via Benchmarking and Debugging. Frank Schneider
2021	J	`jnl`	Cockpit: A Practical Debugging Tool for Training Deep Neural Networks. CoRR Frank Schneider, Felix Dangel, Philipp Hennig
2021	A*	`conf`	Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. NeurIPS Frank Schneider, Felix Dangel, Philipp Hennig
2021	A*	`conf`	Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. ICML Robin M. Schmidt, Frank Schneider, Philipp Hennig
2020	J	`jnl`	Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. CoRR Robin M. Schmidt, Frank Schneider, Philipp Hennig
2019	—	`conf`	DeepOBS: A Deep Learning Optimizer Benchmark Suite. ICLR (Poster) Frank Schneider, Lukas Balles, Philipp Hennig
2019	J	`jnl`	DeepOBS: A Deep Learning Optimizer Benchmark Suite. CoRR Frank Schneider, Lukas Balles, Philipp Hennig