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Lukas Balles
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2020 – today
- 2024
- [j1]Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau:
On the Choice of Learning Rate for Local SGD. Trans. Mach. Learn. Res. 2024 (2024) - [i15]Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella:
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need. CoRR abs/2406.03216 (2024) - [i14]Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Y. Prince, Björn Deiseroth, Andrés Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr:
u-μP: The Unit-Scaled Maximal Update Parametrization. CoRR abs/2407.17465 (2024) - 2023
- [c7]Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella:
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR 2023 - [i13]Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella:
Renate: A Library for Real-World Continual Learning. CoRR abs/2304.12067 (2023) - [i12]Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella:
Continual Learning with Low Rank Adaptation. CoRR abs/2311.17601 (2023) - [i11]Lukas Balles, Cédric Archambeau, Giovanni Zappella:
A Negative Result on Gradient Matching for Selective Backprop. CoRR abs/2312.05021 (2023) - 2022
- [i10]Lukas Balles, Giovanni Zappella, Cédric Archambeau:
Gradient-Matching Coresets for Rehearsal-Based Continual Learning. CoRR abs/2203.14544 (2022) - [i9]Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella:
PASHA: Efficient HPO with Progressive Resource Allocation. CoRR abs/2207.06940 (2022) - 2021
- [i8]Lukas Balles, Giovanni Zappella, Cédric Archambeau:
Gradient-matching coresets for continual learning. CoRR abs/2112.05025 (2021) - 2020
- [c6]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS 2020: 60-69 - [i7]Lukas Balles, Fabian Pedregosa, Nicolas Le Roux:
The Geometry of Sign Gradient Descent. CoRR abs/2002.08056 (2020) - [i6]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR abs/2011.04803 (2020)
2010 – 2019
- 2019
- [c5]Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black:
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation. CVPR 2019: 12240-12249 - [c4]Frank Schneider, Lukas Balles, Philipp Hennig:
DeepOBS: A Deep Learning Optimizer Benchmark Suite. ICLR (Poster) 2019 - [c3]Frederik Kunstner, Philipp Hennig, Lukas Balles:
Limitations of the empirical Fisher approximation for natural gradient descent. NeurIPS 2019: 4158-4169 - [i5]Frank Schneider, Lukas Balles, Philipp Hennig:
DeepOBS: A Deep Learning Optimizer Benchmark Suite. CoRR abs/1903.05499 (2019) - [i4]Frederik Kunstner, Lukas Balles, Philipp Hennig:
Limitations of the Empirical Fisher Approximation. CoRR abs/1905.12558 (2019) - 2018
- [c2]Lukas Balles, Philipp Hennig:
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. ICML 2018: 413-422 - 2017
- [c1]Lukas Balles, Javier Romero, Philipp Hennig:
Coupling Adaptive Batch Sizes with Learning Rates. UAI 2017 - [i3]Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig:
Early Stopping without a Validation Set. CoRR abs/1703.09580 (2017) - [i2]Lukas Balles, Philipp Hennig:
Follow the Signs for Robust Stochastic Optimization. CoRR abs/1705.07774 (2017) - 2016
- [i1]Lukas Balles, Javier Romero, Philipp Hennig:
Coupling Adaptive Batch Sizes with Learning Rates. CoRR abs/1612.05086 (2016)
Coauthor Index
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last updated on 2024-08-25 19:15 CEST by the dblp team
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