| 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 | 681 | 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 | 219 | 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 | 186 | 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 | 135 | 2017 |
| Early stopping without a validation set M Mahsereci, L Balles, C Lassner, P Hennig arXiv preprint arXiv:1703.09580, 2017 | 123 | 2017 |
| DeepOBS: A Deep Learning Optimizer Benchmark Suite F Schneider, L Balles, P Hennig Seventh International Conference on Learning Representations (ICLR), 2019 | 73 | 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 | 19 | 2022 |
| The Geometry of Sign Gradient Descent L Balles, F Pedregosa, N Le Roux arXiv preprint arXiv:2002.08056, 2020 | 19 | 2020 |
| Self-tuning stochastic optimization with curvature-aware gradient filtering RTQ Chen, D Choi, L Balles, D Duvenaud, P Hennig PMLR, 2020 | 9 | 2020 |
| Continual learning with low rank adaptation M Wistuba, PT Sivaprasad, L Balles, G Zappella arXiv preprint arXiv:2311.17601, 2023 | 7 | 2023 |
| Holographic and other point set distances for machine learning L Balles, T Fischbacher | 6 | 2019 |
| Renate: A library for real-world continual learning M Wistuba, M Ferianc, L Balles, C Archambeau, G Zappella arXiv preprint arXiv:2304.12067, 2023 | 4 | 2023 |
| Gradient-matching coresets for rehearsal-based continual learning L Balles, G Zappella, C Archambeau arXiv preprint arXiv:2203.14544, 2022 | 4 | 2022 |
| Automating stochastic optimization with gradient variance estimates L Balles, M Mahsereci, P Hennig ICML AutoML Workshop, 13, 2017 | 4 | 2017 |
| u-P: The Unit-Scaled Maximal Update Parametrization C Blake, C Eichenberg, J Dean, L Balles, LY Prince, B Deiseroth, ... arXiv preprint arXiv:2407.17465, 2024 | 2 | 2024 |
| Gradient-matching coresets for continual learning L Balles, G Zappella, C Archambeau arXiv preprint arXiv:2112.05025, 2021 | 2 | 2021 |
| 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 | 1 | 2024 |
| On the Choice of Learning Rate for Local SGD L Balles, C Archambeau Transactions on Machine Learning Research, 2024 | 1 | 2024 |
| 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 |
| A negative result on gradient matching for selective backprop L Balles, C Archambeau, G Zappella arXiv preprint arXiv:2312.05021, 2023 | | 2023 |
Philipp HennigUniversity of TübingenVerified email at uni-tuebingen.de
