Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation A Ranjan, V Jampani, L Balles, K Kim, D Sun, J Wulff, MJ Black
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
723 2019 Limitations of the empirical fisher approximation for natural gradient descent F Kunstner, P Hennig, L Balles
Advances in neural information processing systems 32, 2019
241 2019 Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients L Balles, P Hennig
Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018
202 2018 Coupling Adaptive Batch Sizes with Learning Rates L Balles, J Romero, P Hennig
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial …, 2017
143 2017 Early stopping without a validation set M Mahsereci, L Balles, C Lassner, P Hennig
arXiv preprint arXiv:1703.09580, 2017
122 2017 DeepOBS: A Deep Learning Optimizer Benchmark Suite F Schneider, L Balles, P Hennig
Seventh International Conference on Learning Representations (ICLR), 2019
80 2019 PASHA: Efficient HPO and NAS with progressive resource allocation O Bohdal, L Balles, M Wistuba, B Ermis, C Archambeau, G Zappella
arXiv preprint arXiv:2207.06940, 2022
22 2022 The Geometry of Sign Gradient Descent L Balles, F Pedregosa, N Le Roux
arXiv preprint arXiv:2002.08056, 2020
22 2020 Continual learning with low rank adaptation M Wistuba, PT Sivaprasad, L Balles, G Zappella
arXiv preprint arXiv:2311.17601, 2023
13 2023 Self-tuning stochastic optimization with curvature-aware gradient filtering RTQ Chen, D Choi, L Balles, D Duvenaud, P Hennig
PMLR, 2020
8 2020 u- C Blake, C Eichenberg, J Dean, L Balles, LY Prince, B Deiseroth, ...
arXiv preprint arXiv:2407.17465, 2024
7 2024 Renate: A library for real-world continual learning M Wistuba, M Ferianc, L Balles, C Archambeau, G Zappella
arXiv preprint arXiv:2304.12067, 2023
6 2023 Gradient-matching coresets for rehearsal-based continual learning L Balles, G Zappella, C Archambeau
arXiv preprint arXiv:2203.14544, 2022
6 2022 Holographic and other point set distances for machine learning L Balles, T Fischbacher
6 2019 Automating stochastic optimization with gradient variance estimates L Balles, M Mahsereci, P Hennig
ICML AutoML Workshop, 13, 2017
4 2017 Choice of peft technique in continual learning: Prompt tuning is not all you need M Wistuba, PT Sivaprasad, L Balles, G Zappella
arXiv preprint arXiv:2406.03216, 2024
2 2024 On the choice of learning rate for local SGD L Balles, C Archambeau
Transactions on Machine Learning Research, 2024
2 2024 Gradient-matching coresets for continual learning L Balles, G Zappella, C Archambeau
arXiv preprint arXiv:2112.05025, 2021
2 2021 Hierarchical Autoregressive Transformers: Combining Byte-and Word-Level Processing for Robust, Adaptable Language Models P Neitemeier, B Deiseroth, C Eichenberg, L Balles
arXiv preprint arXiv:2501.10322, 2025
2025 Continual machine learning in a provider network G Zappella, LS Balles, B Ermis, M Wistuba, CP Archambeau
US Patent App. 17/937,319, 2024
2024 
Philipp HennigUniversity of TübingenE-mail confirmado em uni-tuebingen.de