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Gradient-matching coresets for continual learning

Dec 09, 2021
Lukas Balles, Giovanni Zappella, Cédric Archambeau

* Accepted at the NeurIPS '21 Workshop on Distribution Shifts 

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Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering

Nov 09, 2020
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

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The Geometry of Sign Gradient Descent

Feb 19, 2020
Lukas Balles, Fabian Pedregosa, Nicolas Le Roux

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Limitations of the Empirical Fisher Approximation

May 29, 2019
Frederik Kunstner, Lukas Balles, Philipp Hennig

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DeepOBS: A Deep Learning Optimizer Benchmark Suite

Mar 13, 2019
Frank Schneider, Lukas Balles, Philipp Hennig

* Accepted at ICLR 2019. 9 pages, 3 figures, 2 tables 

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Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

Jun 20, 2018
Lukas Balles, Philipp Hennig

* Presented at the 35th International Conference on Machine Learning (ICML), 2018 

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Coupling Adaptive Batch Sizes with Learning Rates

Jun 28, 2017
Lukas Balles, Javier Romero, Philipp Hennig

* Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI), 2017, (accepted) 

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Early Stopping without a Validation Set

Jun 06, 2017
Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig

* 16 pages, 10 figures 

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