Indro Spinelli

Orcid: 0000-0003-1963-3548

According to our database1, Indro Spinelli authored at least 25 papers between 2017 and 2024.

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Bibliography

2024
A Meta-Learning Approach for Training Explainable Graph Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., April, 2024

Machine Un-learning: An Overview of Techniques, Applications, and Future Directions.
Cogn. Comput., March, 2024

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence.
Cogn. Comput., January, 2024

Social EgoMesh Estimation.
CoRR, 2024

MoDiPO: text-to-motion alignment via AI-feedback-driven Direct Preference Optimization.
CoRR, 2024

Following the Human Thread in Social Navigation.
CoRR, 2024

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.
CoRR, 2024

Adaptive Point Transformer.
CoRR, 2024

OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras.
Proceedings of the Pattern Recognition - 27th International Conference, 2024

From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Length-Aware Motion Synthesis via Latent Diffusion.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
Drop edges and adapt: A fairness enforcing fine-tuning for graph neural networks.
Neural Networks, October, 2023

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks.
IEEE Trans. Ind. Informatics, March, 2023

GATSY: Graph Attention Network for Music Artist Similarity.
CoRR, 2023


Explainability in subgraphs-enhanced Graph Neural Networks.
Proceedings of the 2023 Northern Lights Deep Learning Workshop, 2023

ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo.
Proceedings of the Image Analysis and Processing - ICIAP 2023 Workshops, 2023

Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning.
IEEE Trans. Artif. Intell., 2022

2021
Distributed Training of Graph Convolutional Networks.
IEEE Trans. Signal Inf. Process. over Networks, 2021

Adaptive Propagation Graph Convolutional Network.
IEEE Trans. Neural Networks Learn. Syst., 2021

Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning.
CoRR, 2021

2020
Missing data imputation with adversarially-trained graph convolutional networks.
Neural Networks, 2020

Distributed Graph Convolutional Networks.
CoRR, 2020

2017
Efficient Data Augmentation Using Graph Imputation Neural Networks.
Proceedings of the Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2017


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