3 citations · 6 across the 4 of their papers we have counts for
10 papers
Graph State-Space Models and Latent Relational Inference
Daniele Zambon, Andrea Cini, Cesare Alippi
State-space models effectively model multivariate time series by updating over time a representation of the system state from which predictions are made. The state representation i…
Assessment of Spatio-Temporal Predictors in the Presence of Missing and Heterogeneous Data
Daniele Zambon, Cesare Alippi
Deep learning methods achieve remarkable predictive performance in modeling complex, large-scale data. However, assessing the quality of derived models has become increasingly chal…
BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
Luca Colombo, Fabrizio Pittorino, Daniele Zambon +3
Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy…
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Daniele Zambon, Michele Cattaneo, Ivan Marisca +3
Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While…
Online Continual Graph Learning
Giovanni Donghi, Luca Pasa, Daniele Zambon +2
Continual Learning (CL) aims to incrementally acquire new knowledge while mitigating catastrophic forgetting. Within this setting, Online Continual Learning (OCL) focuses on updati…
The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning
Giovanni Donghi, Daniele Zambon, Luca Pasa +2
Catastrophic forgetting is one of the main obstacles for Online Continual Graph Learning (OCGL), where nodes arrive one by one, distribution drifts may occur at any time and offlin…