| Neural Symbolic Regression that Scales L Biggio, T Bendinelli, A Neitz, A Lucchi, G Parascandolo International Conference on Machine Learning (ICML) 2021, 2021 | 86 | 2021 |
| Prognostics and health management of industrial assets: Current progress and road ahead L Biggio, I Kastanis Frontiers in Artificial Intelligence 3, 578613, 2020 | 69 | 2020 |
| Uncertainty-aware prognosis via deep gaussian process L Biggio, A Wieland, MA Chao, I Kastanis, O Fink IEEE Access 9, 123517-123527, 2021 | 32* | 2021 |
| FIGARO: Controllable Music Generation using Learned and Expert Features D von Rütte, L Biggio, Y Kilcher, T Hofmann The Eleventh International Conference on Learning Representations, 2022 | 24* | 2022 |
| Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse L Noci, S Anagnostidis, L Biggio, A Orvieto, SP Singh, A Lucchi Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 16 | 2022 |
| Ageing-aware battery discharge prediction with deep learning L Biggio, T Bendinelli, C Kulkarni, O Fink Applied Energy 346, 121229, 2023 | 11* | 2023 |
| A seq2seq approach to symbolic regression L Biggio, T Bendinelli, A Lucchi, G Parascandolo Learning Meets Combinatorial Algorithms at NeurIPS2020, 2020 | 10 | 2020 |
| Phme data challenge 2021 L Biggio, M Russi, S Bigdeli, I Kastanis, D Giordano, D Gagar 7th European Conference of the PHM Society, 2021 | 6* | 2021 |
| Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial V Nemani, L Biggio, X Huan, Z Hu, O Fink, A Tran, Y Wang, X Zhang, ... Mechanical Systems and Signal Processing 205, 110796, 2023 | 4 | 2023 |
| On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics EM Compagnoni, A Scampicchio, L Biggio, A Orvieto, T Hofmann, ... 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 4* | 2023 |
| An SDE for Modeling SAM: Theory and Insights E Monzio Compagnoni, A Orvieto, L Biggio, H Kersting, FN Proske, ... arXiv e-prints, arXiv: 2301.08203, 2023 | 2* | 2023 |
| Fast emulation of two-point angular statistics for photometric galaxy surveys M Bonici, L Biggio, C Carbone, L Guzzo arXiv preprint arXiv:2206.14208, 2022 | 2 | 2022 |
| Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers S Anagnostidis, D Pavllo, L Biggio, L Noci, A Lucchi, T Hoffmann NeurIPS 2023 (spotlight), 2023 | 1 | 2023 |
| Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses L Biggio, G Vernardos, A Galan, A Peel arXiv preprint arXiv:2210.09169, 2022 | 1 | 2022 |
| Time delay estimation in unresolved lensed quasars L Biggio, A Domi, S Tosi, G Vernardos, D Ricci, L Paganin, G Bracco Monthly Notices of the Royal Astronomical Society, 2022 | 1 | 2022 |
| Weight Uncertainty in Neural Networks G Flamich, T Matejovicová, L Biggio Proc. Int. Conf. Mach. Learn, 1613-1622, 2019 | 1 | 2019 |
| Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization E Benarous, S Anagnostidis, L Biggio, T Hofmann arXiv preprint arXiv:2311.06224, 2023 | | 2023 |
| Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematics analyses MR Gomer, S Ertl, L Biggio, H Wang, A Galan, L Van de Vyvere, D Sluse, ... arXiv preprint arXiv:2307.10381, 2023 | | 2023 |
| Gemtelligence: Accelerating Gemstone classification with Deep Learning T Bendinelli, L Biggio, D Nyfeler, A Ghosh, P Tollan, MA Kirschmann, ... arXiv preprint arXiv:2306.06069, 2023 | | 2023 |
| Controllable Neural Symbolic Regression T Bendinelli, L Biggio, PA Kamienny arXiv preprint arXiv:2304.10336, 2023 | | 2023 |
Tommaso BendinelliCSEMVerified email at csem.ch
