1 citations · 1 across the 2 of their papers we have counts for
8 papers
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…
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…
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…
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs
Daniele Zambon, Cesare Alippi
We present the first whiteness test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph. The statistical test aims at findi…
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon, Cesare Alippi, Lorenzo Livi
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding natura…
Autoregressive Models for Sequences of Graphs
Daniele Zambon, Daniele Grattarola, Lorenzo Livi +1
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very g…