An Investigation on Legal Regulations for Robot Deployment in Urban Areas: A Focus on Italian Law. - Pericle Salvini; Giancarlo Teti; Enza Spadoni; Emiliano Frediani; Silvio Boccalatte; Luca Nocco; Barbara Mazzolai; Cecilia Laschi; Giovanni Comandè; Emanuele Rossi; Paolo Carrozza; Paolo Dario (Adv. Robotics, 2010)

Numerical simulation of 2D-vorticity dynamics using particle methods. - Emanuele Rossi; Andrea Colagrossi; Giorgio Graziani (Comput. Math. Appl., 2015)

Numerical simulations of the transition from laminar to chaotic behaviour of the planar vortex flow past a circular cylinder. - Danilo Durante; Emanuele Rossi; Andrea Colagrossi; Giorgio Graziani (Commun. Nonlinear Sci. Numer. Simul., 2017)

ncRNA Classification with Graph Convolutional Networks. - Emanuele Rossi; Federico Monti; Michael M. Bronstein; Pietro Liò (CoRR, 2019)

Tuning Word2vec for Large Scale Recommendation Systems. - Benjamin Paul Chamberlain; Emanuele Rossi; Dan Shiebler; Suvash Sedhain; Michael M. Bronstein (RecSys, 2020)

Bifurcations and chaos transition of the flow over an airfoil at low Reynolds number varying the angle of attack. - Danilo Durante; Emanuele Rossi; Andrea Colagrossi (Commun. Nonlinear Sci. Numer. Simul., 2020)

SIGN: Scalable Inception Graph Neural Networks. - Emanuele Rossi; Fabrizio Frasca; Ben Chamberlain 001; Davide Eynard; Michael M. Bronstein; Federico Monti (CoRR, 2020)

Temporal Graph Networks for Deep Learning on Dynamic Graphs. - Emanuele Rossi; Ben Chamberlain 001; Fabrizio Frasca; Davide Eynard; Federico Monti; Michael M. Bronstein (CoRR, 2020)

GRAND: Graph Neural Diffusion. - Ben Chamberlain 001; James Rowbottom; Maria I. Gorinova 001; Michael M. Bronstein; Stefan Webb; Emanuele Rossi (ICML, 2021)

Learning to Infer Structures of Network Games. - Emanuele Rossi; Federico Monti; Yan Leng; Michael M. Bronstein; Xiaowen Dong 001 (ICML, 2022)

On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. - Emanuele Rossi; Henry Kenlay; Maria I. Gorinova 001; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (LoG, 2022)

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. - Mirco Mutti; Riccardo De Santi; Emanuele Rossi; Juan Felipe Calderón; Michael M. Bronstein; Marcello Restelli (AAAI, 2023)

Graph Neural Networks for Link Prediction with Subgraph Sketching. - Benjamin Paul Chamberlain; Sergey Shirobokov; Emanuele Rossi; Fabrizio Frasca; Thomas Markovich; Nils Yannick Hammerla; Michael M. Bronstein; Max Hansmire (ICLR, 2023)

Edge Directionality Improves Learning on Heterophilic Graphs. - Emanuele Rossi; Bertrand Charpentier; Francesco Di Giovanni; Fabrizio Frasca; Stephan Günnemann; Michael M. Bronstein (LoG, 2023)

Temporal Graph Benchmark for Machine Learning on Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Jacob Danovitch; Matthias Fey; Weihua Hu; Emanuele Rossi; Jure Leskovec; Michael M. Bronstein; Guillaume Rabusseau; Reihaneh Rabbany (NeurIPS, 2023)

Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? - Daniele Malitesta; Emanuele Rossi; Claudio Pomo; Tommaso Di Noia; Fragkiskos D. Malliaros (CIKM, 2024)

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Reihaneh Rabbany; Guillaume Rabusseau; Emanuele Rossi (LoG, 2024)

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs. - Julia Gastinger; Shenyang Huang; Michael Galkin; Erfan Loghmani; Ali Parviz; Farimah Poursafaei; Jacob Danovitch; Emanuele Rossi; Ioannis Koutis; Heiner Stuckenschmidt; Reihaneh Rabbany; Guillaume Rabusseau (NeurIPS, 2024)

Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach. - Daniele Malitesta; Emanuele Rossi; Claudio Pomo; Fragkiskos D. Malliaros; Tommaso Di Noia (CoRR, 2024)

Channel Balance Interpolation in the Lightning Network via Machine Learning. - Vincent; Emanuele Rossi; Vikash Singh (CoRR, 2024)

Bayesian Binary Search. - Vikash Singh; Matthew Khanzadeh; Vincent Davis; Harrison Rush; Emanuele Rossi; Jesse Shrader; Pietro Lio (CoRR, 2024)