Luca Biggio

According to our database, Luca Biggio authored at least 21 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens.

Vittorio Erba, Emanuele Troiani, Luca Biggio, Antoine Maillard, Lenka Zdeborová

CoRR, 2024

Understanding Counting in Small Transformers: The Interplay between Attention and Feed-Forward Layers.

Freya Behrens, Luca Biggio, Lenka Zdeborová

CoRR, 2024

2023

Transformers beyond natural language: a theoretical and empirical investigation.

Luca Biggio

PhD thesis, 2023

Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization.

Elior Benarous, Sotiris Anagnostidis, Luca Biggio, Thomas Hofmann

CoRR, 2023

Gemtelligence: Accelerating Gemstone classification with Deep Learning.

Tommaso Bendinelli, Luca Biggio, Daniel Nyfeler, Abhigyan Ghosh, Peter Tollan, Moritz Alexander Kirschmann, Olga Fink

CoRR, 2023

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial.

Venkat Nemani, Luca Biggio, Xun Huan, Zhen Hu, Olga Fink, Anh Tran, Yan Wang, Xiaoping Du, Xiaoge Zhang, Chao Hu

CoRR, 2023

Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers.

Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics.

Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann

Proceedings of the International Joint Conference on Neural Networks, 2023

An SDE for Modeling SAM: Theory and Insights.

Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi

Proceedings of the International Conference on Machine Learning, 2023

Controllable Neural Symbolic Regression.

Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny

Proceedings of the International Conference on Machine Learning, 2023

FIGARO: Controllable Music Generation using Learned and Expert Features.

Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Cosmology from Galaxy Redshift Surveys with PointNet.

Sotiris Anagnostidis, Arne Thomsen, Tomasz Kacprzak, Tilman Tröster, Luca Biggio, Alexandre Refregier, Thomas Hofmann

CoRR, 2022

Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction.

Luca Biggio, Tommaso Bendinelli, Chetan S. Kulkarni, Olga Fink

CoRR, 2022

FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control.

Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hoffman

CoRR, 2022

Randomized Signature Layers for Signal Extraction in Time Series Data.

Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann

CoRR, 2022

Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse.

Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Uncertainty-aware Remaining Useful Life predictor.

Luca Biggio, Alexander Wieland, Manuel Arias Chao, Iason Kastanis, Olga Fink

CoRR, 2021

Uncertainty-Aware Prognosis via Deep Gaussian Process.

Luca Biggio, Alexander Wieland, Manuel Arias Chao, Iason Kastanis, Olga Fink

IEEE Access, 2021

Self-supervised pre-training on industrial time-series.

Luca Biggio, Iason Kastanis

Proceedings of the 8th Swiss Conference on Data Science, 2021

Neural Symbolic Regression that scales.

Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead.

Luca Biggio, Iason Kastanis

Frontiers Artif. Intell., 2020