Per Year
@incollection{skiqs-woa2022, author = {Agiollo, Andrea and Rafanelli, Andrea and Omicini, Andrea}, booktitle = {WOA 2022 -- 23rd Workshop ``From Objects to Agents''}, dblp = {conf/woa/AgiolloRO22}, editor = {Ferrando, Angelo and Mascardi, Viviana}, iris = {11585/899383}, issn = {1613-0073}, keywords = {symbolic knowledge injection, quality of service, efficiency, understandability, robustness}, month = nov, numpages = 18, pages = {30--47}, publisher = {Sun SITE Central Europe, RWTH Aachen University}, scholar = {5478874943780106057}, scopus = {2-s2.0-85142522311}, semanticscholar = {253270240}, series = {CEUR Workshop Proceedings}, subseries = {AIxIA Series}, title = {Towards Quality-of-Service Metrics for Symbolic Knowledge Injection}, url = {http://ceur-ws.org/Vol-3261/paper3.pdf}, urlopenaccess = {http://ceur-ws.org/Vol-3261/paper3.pdf}, urlpdf = {http://ceur-ws.org/Vol-3261/paper3.pdf}, volume = 3261, year = 2022 }
@inproceedings{gnn2gnn-uai2022, author = {Agiollo, Andrea and Omicini, Andrea}, booktitle = {Uncertainty in Artificial Intelligence}, dblp = {conf/uai/AgiolloO22}, editor = {Cussens, James and Zhang, Kun}, iris = {11585/899465}, issn = {2640-3498}, month = aug, note = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, {UAI} 2022, 1-5 August 2022, Eindhoven, The Netherlands}, pages = {32--42}, publisher = {ML Research Press}, scholar = {3643983533846865361}, series = {Proceedings of Machine Learning Research}, title = {{GNN2GNN}: Graph Neural Networks to Generate Neural Networks}, url = {https://proceedings.mlr.press/v180/agiollo22a.html}, urlopenaccess = {https://proceedings.mlr.press/v180/agiollo22a/agiollo22a.pdf}, volume = 180, year = 2022 }
@incollection{shallow2deep-extraamas2021, address = {Cham, Switzerland}, author = {Agiollo, Andrea and Ciatto, Giovanni and Omicini, Andrea}, booktitle = {Explainable and Transparent AI and Multi-Agent Systems. Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3--7, 2021, Revised Selected Papers}, dblp = {conf/atal/AgiolloCO21}, doi = {10.1007/978-3-030-82017-6_5}, editor = {Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Fr{\"a}mling, Kary}, eisbn = {978-3-030-82017-6}, eissn = {1611-3349}, iris = {11585/838540}, isbn = {978-3-030-82016-9}, issn = {0302-9743}, keywords = {Neural Architecture Search; Evolutionary Algorithm; Opacity; Interpretability}, numpages = 20, pages = {63--82}, publisher = {Springer}, scholar = {8606811357246615623}, scopus = {2-s2.0-85113325735}, semanticscholar = {236460263}, series = {Lecture Notes in Computer Science}, subseries = {Lecture Notes in Artificial Intelligence}, title = {{\it Shallow2Deep}: Restraining Neural Networks Opacity through Neural Architecture Search}, url = {http://link.springer.com/10.1007/978-3-030-82017-6_5}, volume = 12688, wos = {000691781800005}, year = 2021 }
@article{nnconstrained-applsci11, articleno = 11957, author = {Agiollo, Andrea and Omicini, Andrea}, doi = {10.3390/app112411957}, iris = {11585/842440}, issn = {2076-3417}, journal = {Applied Sciences}, keywords = {Load Classification; Neural Networks; Embedding; Hyper-constrained Devices}, month = dec, note = {Special Issue ``Artificial Intelligence and Data Engineering in Engineering Applications''}, number = 24, publisher = {MDPI}, scholar = {14515352047550375229}, scopus = {2-s2.0-85121296114}, title = {Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices}, url = {https://www.mdpi.com/2076-3417/11/24/11957}, url-openaccess = {https://www.mdpi.com/2076-3417/11/24/11957/pdf}, urlopenaccess = {https://www.mdpi.com/2076-3417/11/24/11957/pdf}, urlpdf = {https://www.mdpi.com/2076-3417/11/24/11957/pdf}, volume = 11, wos = {000735509300001}, year = 2021 }
@article{detonar-ieetnsm2021, author = {Agiollo, Andrea and Conti, Mauro and Kaliyar, Pallavi and Lin, TsungNan and Pajola, Luca}, doi = {10.1109/TNSM.2021.3075496}, ieee = {9415869}, iris = {11585/842654}, issn = {1932-4537}, journal = {IEEE Transactions on Network and Service Management}, keywords = {Internet of Things, Low Power and Lossy Networks, Routing Protocol, Networking attacks, Intrusion Detection System}, number = 2, numpages = 13, pages = {1178 - 1190}, publisher = {IEEE}, title = {{DETONAR}: Detection of Routing Attacks in {RPL}-based {I}o{T}}, url = {https://ieeexplore.ieee.org/document/9415869}, urlpdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9415869}, volume = 18, wos = {000660636700006}, year = 2021 }
@inproceedings{gnn-woa2021, articleno = 8, author = {Agiollo, Andrea and Ciatto, Giovanni and Omicini, Andrea}, booktitle = {WOA 2021 -- 22nd Workshop ``From Objects to Agents''}, dblp = {conf/woa/AgiolloCO21}, editor = {Calegari, Roberta and Ciatto, Giovanni and Denti, Enrico and Omicini, Andrea and Sartor, Giovanni}, iris = {11585/834362}, issn = {1613-0073}, keywords = {Graph Neural Networks, Machine Learning, Embedding, Computational Logic}, location = {Bologna, Italy}, month = oct, note = {22nd Workshop ``From Objects to Agents'' (WOA 2021), Bologna, Italy, 1--3~} # sep # {~2021. Proceedings}, numpages = 18, pages = {98--115}, publisher = {Sun SITE Central Europe, RWTH Aachen University}, scholar = {817372786663443317}, scopus = {2-s2.0-85116916925}, semanticscholar = {238360605}, series = {CEUR Workshop Proceedings}, subseries = {AI*IA Series}, title = {Graph Neural Networks as the Copula Mundi between Logic and Machine Learning: A Roadmap}, url = {http://ceur-ws.org/Vol-2963/paper18.pdf}, volume = 2963, year = 2021 }
@inproceedings{rgbdsemanticsegmentation-icdsc2019, articleno = 9, author = {Michieli, Umberto and Camporese, Maria and Agiollo, Andrea and Pagnutti, Giampaolo and Zanuttigh, Pietro}, booktitle = {13th International Conference on Distributed Smart Cameras (ICDSC2019)}, dblp = {conf/icdsc/MichieliCAPZ19}, doi = {10.1145/3349801.3349810}, editor = {Conci, Nicola and Shan, Caifeng and Marcenaro, Lucio and Han, Jungong}, iris = {11585/842656}, isbn = {978-1-4503-7189-6}, location = {Trento, Italy}, month = {9--11}}}}, title = {Region Merging Driven by Deep Learning for {RGB-D} Segmentation and Labeling}, url = {https://doi.org/10.1145/3349801.3349810}, wos = {000519116500009}, year = 2019 }