40 citations · 98 across the 9 of their papers we have counts for
4 papers · 1 filter
Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting
Valentina Moretti, Ivan Marisca, Cesare Alippi +1
Deep learning models have grown popular in time series applications. However, the large quantity of newly proposed architectures and the often contradictory empirical results make…
Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
Carlo Abate, Ivan Marisca, Filippo Maria Bianchi
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, wha…
Over-squashing in Spatiotemporal Graph Neural Networks
Ivan Marisca, Jacob Bamberger, Cesare Alippi +1
Graph Neural Networks (GNNs) have achieved remarkable success across various domains. However, recent theoretical advances have identified fundamental limitations in their informat…
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…