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Daniele Zambon
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2020 – today
- 2024
- [j6]Ming Jin
, Huan Yee Koh
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 10466-10485 (2024) - [j5]Daniele Grattarola
Understanding Pooling in Graph Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2708-2718 (2024) - [c14]Giovanni de Felice, Andrea Cini, Daniele Zambon, Vladimir V. Gusev, Cesare Alippi:
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations. ICLR 2024 - [c13]Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi:
Temporal Graph ODEs for Irregularly-Sampled Time Series. IJCAI 2024: 4025-4034 - [i18]Giovanni de Felice, Andrea Cini, Daniele Zambon, Vladimir V. Gusev, Cesare Alippi:
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations. CoRR abs/2402.12598 (2024) - [i17]Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi:
Temporal Graph ODEs for Irregularly-Sampled Time Series. CoRR abs/2404.19508 (2024) - [i16]Alessandro Manenti, Daniele Zambon, Cesare Alippi:
Learning Latent Graph Structures and their Uncertainty. CoRR abs/2405.19933 (2024) - 2023
- [j4]Andrea Cini, Daniele Zambon, Cesare Alippi:
Sparse Graph Learning from Spatiotemporal Time Series. J. Mach. Learn. Res. 24: 242:1-242:36 (2023) - [c12]Davide Bacciu, Federico Errica, Alessio Micheli
Graph Representation Learning. ESANN 2023 - [c11]Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi:
Taming Local Effects in Graph-based Spatiotemporal Forecasting. NeurIPS 2023 - [i15]Daniele Zambon, Andrea Cini, Lorenzo Livi, Cesare Alippi:
Graph state-space models. CoRR abs/2301.01741 (2023) - [i14]Daniele Zambon, Cesare Alippi:
Where and How to Improve Graph-based Spatio-temporal Predictors. CoRR abs/2302.01701 (2023) - [i13]Andrea Cini, Ivan Marisca
Taming Local Effects in Graph-based Spatiotemporal Forecasting. CoRR abs/2302.04071 (2023) - [i12]Cesare Alippi, Daniele Zambon:
Graph Kalman Filters. CoRR abs/2303.12021 (2023) - [i11]Ming Jin, Huan Yee Koh, Qingsong Wen
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection. CoRR abs/2307.03759 (2023) - [i10]Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi:
Graph Deep Learning for Time Series Forecasting. CoRR abs/2310.15978 (2023) - 2022
- [c10]Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon:
Deep Learning for Graphs. ESANN 2022 - [c9]Matteo Munari, Luca Pasa, Daniele Zambon
Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning. IJCNN 2022: 1-8 - [c8]Daniele Zambon
Graph iForest: Isolation of anomalous and outlier graphs. IJCNN 2022: 1-8 - [c7]Daniele Zambon, Cesare Alippi:
AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs. NeurIPS 2022 - [i9]Daniele Zambon, Cesare Alippi:
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs. CoRR abs/2204.11135 (2022) - [i8]Andrea Cini
Sparse Graph Learning for Spatiotemporal Time Series. CoRR abs/2205.13492 (2022) - 2021
- [c6]Benjamin Paassen
Graph Edit Networks. ICLR 2021 - [p1]Daniele Zambon:
Erkennung von Anomalien und Veränderung in Graphsequenzen. Ausgezeichnete Informatikdissertationen 2021: 311-320 - [i7]Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi, Cesare Alippi:
Understanding Pooling in Graph Neural Networks. CoRR abs/2110.05292 (2021) - 2020
- [j3]Daniele Grattarola
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds. IEEE Trans. Neural Networks Learn. Syst. 31(6): 1856-1869 (2020) - [c5]Daniele Zambon, Cesare Alippi, Lorenzo Livi:
Graph Random Neural Features for Distance-Preserving Graph Representations. ICML 2020: 10968-10977
2010 – 2019
- 2019
- [j2]Daniele Zambon
Change-Point Methods on a Sequence of Graphs. IEEE Trans. Signal Process. 67(24): 6327-6341 (2019) - [c4]Daniele Zambon
Autoregressive Models for Sequences of Graphs. IJCNN 2019: 1-8 - [i6]Daniele Zambon, Daniele Grattarola, Lorenzo Livi, Cesare Alippi:
Autoregressive Models for Sequences of Graphs. CoRR abs/1903.07299 (2019) - [i5]Daniele Zambon, Cesare Alippi, Lorenzo Livi:
Distance-Preserving Graph Embeddings from Random Neural Features. CoRR abs/1909.03790 (2019) - 2018
- [j1]Daniele Zambon
Concept Drift and Anomaly Detection in Graph Streams. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5592-5605 (2018) - [c3]Daniele Zambon
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings. IJCNN 2018: 1-7 - [i4]Daniele Zambon, Lorenzo Livi, Cesare Alippi:
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings. CoRR abs/1805.01360 (2018) - [i3]Daniele Grattarola, Daniele Zambon, Cesare Alippi, Lorenzo Livi:
Learning Graph Embeddings on Constant-Curvature Manifolds for Change Detection in Graph Streams. CoRR abs/1805.06299 (2018) - [i2]Daniele Zambon, Cesare Alippi, Lorenzo Livi:
Change Point Methods on a Sequence of Graphs. CoRR abs/1805.07113 (2018) - 2017
- [c2]Daniele Zambon
Detecting changes in sequences of attributed graphs. SSCI 2017: 1-7 - [i1]Daniele Zambon, Cesare Alippi, Lorenzo Livi:
Concept Drift and Anomaly Detection in Graph Streams. CoRR abs/1706.06941 (2017) - 2016
- [c1]Diego Carrera, Beatrice Rossi, Daniele Zambon
ECG Monitoring in Wearable Devices by Sparse Models. ECML/PKDD (3) 2016: 145-160
Coauthor Index
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