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Luca Biggio
Luca Biggio
Research fellow, EPFL
Verified email at epfl.ch
Title
Cited by
Cited by
Year
Neural Symbolic Regression that Scales
L Biggio, T Bendinelli, A Neitz, A Lucchi, G Parascandolo
International Conference on Machine Learning (ICML) 2021, 2021
1122021
Prognostics and health management of industrial assets: Current progress and road ahead
L Biggio, I Kastanis
Frontiers in Artificial Intelligence 3, 578613, 2020
782020
Uncertainty-aware prognosis via deep gaussian process
L Biggio, A Wieland, MA Chao, I Kastanis, O Fink
IEEE Access 9, 123517-123527, 2021
38*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
33*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
252022
Ageing-aware battery discharge prediction with deep learning
L Biggio, T Bendinelli, C Kulkarni, O Fink
Applied Energy 346, 121229, 2023
18*2023
A seq2seq approach to symbolic regression
L Biggio, T Bendinelli, A Lucchi, G Parascandolo
Learning Meets Combinatorial Algorithms at NeurIPS2020, 2020
142020
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
112023
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
92023
An SDE for Modeling SAM: Theory and Insights
E Monzio Compagnoni, L Biggio, A Orvieto, FN Proske, H Kersting, ...
arXiv e-prints, arXiv: 2301.08203, 2023
9*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
7*2023
Phme data challenge 2021
L Biggio, M Russi, S Bigdeli, I Kastanis, D Giordano, D Gagar
7th European Conference of the PHM Society, 2021
7*2021
Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematic analyses
MR Gomer, S Ertl, L Biggio, H Wang, A Galan, L Van de Vyvere, D Sluse, ...
Astronomy & Astrophysics 679, A59, 2023
3*2023
Controllable Neural Symbolic Regression
T Bendinelli, L Biggio, PA Kamienny
arXiv preprint arXiv:2304.10336, 2023
3*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
32022
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
32022
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
22022
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
12023
Weight Uncertainty in Neural Networks
G Flamich, T Matejovicová, L Biggio
Proc. Int. Conf. Mach. Learn, 1613-1622, 2019
12019
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
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Articles 1–20