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ORCIDFederico Barbero
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2020 – today
- 2025
[c8]Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein:
Bundle Neural Network for message diffusion on graphs. ICLR 2025- [c7]Federico Barbero, Alex Vitvitskyi, Christos Perivolaropoulos, Razvan Pascanu, Petar Velickovic:
Round and Round We Go! What makes Rotary Positional Encodings useful? ICLR 2025 - [i19]Alvaro Arroyo, Alessio Gravina, Benjamin Gutteridge, Federico Barbero, Claudio Gallicchio, Xiaowen Dong, Michael M. Bronstein, Pierre Vandergheynst:
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning. CoRR abs/2502.10818 (2025) - [i18]Itay Yona, Ilia Shumailov, Jamie Hayes, Federico Barbero, Yossi Gandelsman:
Interpreting the Repeated Token Phenomenon in Large Language Models. CoRR abs/2503.08908 (2025) - [i17]Federico Barbero, Alvaro Arroyo, Xiangming Gu, Christos Perivolaropoulos, Michael M. Bronstein, Petar Velickovic, Razvan Pascanu:
Why do LLMs attend to the first token? CoRR abs/2504.02732 (2025) - [i16]Isaac Reid, Arijit Sehanobish, Cederik Höfs, Bruno Mlodozeniec, Leonhard Vulpius, Federico Barbero, Adrian Weller, Krzysztof Choromanski, Richard E. Turner, Petar Velickovic:
Wavelet-Induced Rotary Encodings: RoPE Meets Graphs. CoRR abs/2509.22259 (2025) - [i15]Enrique Queipo-de-Llano, Alvaro Arroyo, Federico Barbero, Xiaowen Dong, Michael M. Bronstein, Yann LeCun, Ravid Shwartz-Ziv:
Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin. CoRR abs/2510.06477 (2025) - [i14]Federico Barbero, Xiangming Gu, Christopher A. Choquette-Choo, Chawin Sitawarin, Matthew Jagielski, Itay Yona, Petar Velickovic, Ilia Shumailov, Jamie Hayes:
Extracting alignment data in open models. CoRR abs/2510.18554 (2025) - [i13]Vivek Veeriah, Federico Barbero, Marcus Chiam, Xidong Feng, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Johan Obando-Ceron, Jiaxin Shi, Shaobo Hou, Satinder Singh, Nenad Tomasev, Tom Zahavy:
Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions. CoRR abs/2510.23772 (2025) - [i12]Xidong Feng, Vivek Veeriah, Marcus Chiam, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Federico Barbero, Johan Obando-Ceron, Jiaxin Shi, Satinder Singh, Shaobo Hou, Nenad Tomasev, Tom Zahavy:
Generating Creative Chess Puzzles. CoRR abs/2510.23881 (2025) - 2024
- [c6]Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni:
Locality-Aware Graph Rewiring in GNNs. ICLR 2024 - [c5]Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João Guilherme Madeira Araújo, Oleksandr Vitvitskyi, Razvan Pascanu, Petar Velickovic:
Transformers need glasses! Information over-squashing in language tasks. NeurIPS 2024 - [i11]Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein:
Bundle Neural Networks for message diffusion on graphs. CoRR abs/2405.15540 (2024) - [i10]Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João G. M. Araújo, Alex Vitvitskyi, Razvan Pascanu, Petar Velickovic:
Transformers need glasses! Information over-squashing in language tasks. CoRR abs/2406.04267 (2024) - [i9]Petar Velickovic, Christos Perivolaropoulos, Federico Barbero, Razvan Pascanu:
softmax is not enough (for sharp out-of-distribution). CoRR abs/2410.01104 (2024) - [i8]Federico Barbero, Alex Vitvitskyi, Christos Perivolaropoulos, Razvan Pascanu, Petar Velickovic:
Round and Round We Go! What makes Rotary Positional Encodings useful? CoRR abs/2410.06205 (2024) - [i7]Benedict Aaron Tjandra, Federico Barbero, Michael M. Bronstein:
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification. CoRR abs/2411.03596 (2024) - 2023
- [c4]Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò:
Latent Graph Inference using Product Manifolds. ICLR 2023 - [c3]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. ICML 2023: 7865-7885 - [i6]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. CoRR abs/2302.02941 (2023) - [i5]Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni:
Locality-Aware Graph-Rewiring in GNNs. CoRR abs/2310.01668 (2023) - 2022
- [c2]Federico Barbero, Feargus Pendlebury, Fabio Pierazzi
, Lorenzo Cavallaro:
Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift. SP 2022: 805-823 - [c1]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. TAG-ML 2022: 28-36 - [i4]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. CoRR abs/2206.08702 (2022) - [i3]Haitz Sáez de Ocáriz Borde, Federico Barbero:
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression. CoRR abs/2209.13410 (2022) - [i2]Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò:
Latent Graph Inference using Product Manifolds. CoRR abs/2211.16199 (2022) - 2020
- [i1]Federico Barbero, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro:
Transcending Transcend: Revisiting Malware Classification with Conformal Evaluation. CoRR abs/2010.03856 (2020)
Coauthor Index
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