Emanuele Zangrando

According to our database, Emanuele Zangrando authored at least 9 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

dEBORA: Efficient Bilevel Optimization-based low-Rank Adaptation.

Emanuele Zangrando, Sara Venturini, Francesco Rinaldi, Francesco Tudisco

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

GeoLoRA: Geometric integration for parameter efficient fine-tuning.

Steffen Schotthöfer, Emanuele Zangrando, Gianluca Ceruti, Francesco Tudisco, Jonas Kusch

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Low-Rank Adversarial PGD Attack.

Dayana Savostianova, Emanuele Zangrando, Francesco Tudisco

CoRR, 2024

Neural Rank Collapse: Weight Decay and Small Within-Class Variability Yield Low-Rank Bias.

Emanuele Zangrando, Piero Deidda, Simone Brugiapaglia, Nicola Guglielmi, Francesco Tudisco

CoRR, 2024

Geometry-aware training of factorized layers in tensor Tucker format.

Emanuele Zangrando, Steffen Schotthöfer, Gianluca Ceruti, Jonas Kusch, Francesco Tudisco

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Rank-adaptive spectral pruning of convolutional layers during training.

Emanuele Zangrando, Steffen Schotthöfer, Gianluca Ceruti, Jonas Kusch, Francesco Tudisco

CoRR, 2023

HIJACK: Learning-based Strategies for Sound Classification Robustness to Adversarial Noise.

Derek Sweet, Emanuele Zangrando, Francesca Meneghello

Proceedings of the 2023 IEEE International Conference on Smart Computing, 2023

Robust low-rank training via approximate orthonormal constraints.

Dayana Savostianova, Emanuele Zangrando, Gianluca Ceruti, Francesco Tudisco

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

2022

Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations.

Steffen Schotthöfer, Emanuele Zangrando, Jonas Kusch, Gianluca Ceruti, Francesco Tudisco

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