Lukas Balles | DeepAI

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Featured Co-authors

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Cedric Archambeau
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∙ 04/24/2023

Renate: A Library for Real-World Continual Learning

Continual learning enables the incremental training of machine learning ...

4 Martin Wistuba, et al. ∙

∙ 07/14/2022

PASHA: Efficient HPO with Progressive Resource Allocation

Hyperparameter optimization (HPO) and neural architecture search (NAS) a...

0 Ondrej Bohdal, et al. ∙

∙ 03/28/2022

Gradient-Matching Coresets for Rehearsal-Based Continual Learning

The goal of continual learning (CL) is to efficiently update a machine l...

0 Lukas Balles, et al. ∙

∙ 12/09/2021

Gradient-matching coresets for continual learning

We devise a coreset selection method based on the idea of gradient match...

0 Lukas Balles, et al. ∙

∙ 11/09/2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering

Standard first-order stochastic optimization algorithms base their updat...

0 Ricky T. Q. Chen, et al. ∙

∙ 05/29/2019

Limitations of the Empirical Fisher Approximation

Natural gradient descent, which preconditions a gradient descent update ...

0 Frederik Kunstner, et al. ∙

∙ 03/13/2019

DeepOBS: A Deep Learning Optimizer Benchmark Suite

Because the choice and tuning of the optimizer affects the speed, and ul...

0 Frank Schneider, et al. ∙

∙ 05/22/2017

Follow the Signs for Robust Stochastic Optimization

Stochastic noise on gradients is now a common feature in machine learnin...

0 Lukas Balles, et al. ∙

∙ 03/28/2017

Early Stopping without a Validation Set

Early stopping is a widely used technique to prevent poor generalization...

0 Maren Mahsereci, et al. ∙

∙ 12/15/2016

Coupling Adaptive Batch Sizes with Learning Rates

Mini-batch stochastic gradient descent and variants thereof have become ...

0 Lukas Balles, et al. ∙

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