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ORCIDIvano Lauriola
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2020 – today
- 2024
[c25]Stefano Campese, Ivano Lauriola, Alessandro Moschitti:
Pre-Training Methods for Question Reranking. EACL (2) 2024: 469-476- 2023
- [c24]Liang Wang, Ivano Lauriola, Alessandro Moschitti:
Accurate Training of Web-based Question Answering Systems with Feedback from Ranked Users. ACL (industry) 2023: 660-667 - [c23]Stefano Campese, Ivano Lauriola, Alessandro Moschitti:
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval. EMNLP (Findings) 2023: 15573-15587 - [i3]Stefano Campese, Ivano Lauriola, Alessandro Moschitti:
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval. CoRR abs/2304.01003 (2023) - 2022
- [j5]Ivano Lauriola, Alberto Lavelli, Fabio Aiolli:
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing 470: 443-456 (2022) - [c22]Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert:
FocusQA: Open-Domain Question Answering with a Context in Focus. EMNLP (Findings) 2022: 5195-5208 - [c21]Ivano Lauriola, Kevin Small, Alessandro Moschitti:
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification. LREC 2022: 4701-4707 - 2021
- [j4]Ivano Lauriola
, Fabio Aiolli, Alberto Lavelli, Fabio Rinaldi:
Learning adaptive representations for entity recognition in the biomedical domain. J. Biomed. Semant. 12(1): 10 (2021) - [c20]Ivano Lauriola, Alessandro Moschitti:
Answer Sentence Selection Using Local and Global Context in Transformer Models. ECIR (1) 2021: 298-312 - [c19]Ivano Lauriola, Alberto Lavelli, Alessandro Moschitti, Fabio Aiolli:
Exploring the structure of BERT through Kernel Learning. IJCNN 2021: 1-9 - 2020
- [j3]Ivano Lauriola, Claudio Gallicchio, Fabio Aiolli:
Enhancing deep neural networks via multiple kernel learning. Pattern Recognit. 101: 107194 (2020) - [j2]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
Learning deep kernels in the space of monotone conjunctive polynomials. Pattern Recognit. Lett. 140: 200-206 (2020) - [c18]Pasquale Capuozzo, Ivano Lauriola, Carlo Strapparava, Fabio Aiolli, Giuseppe Sartori:
Automatic Detection of Cross-language Verbal Deception. CogSci 2020 - [c17]Ivano Lauriola, Alberto Lavelli, Fabio Aiolli:
Language processing in the era of deep learning. ESANN 2020: 597-606 - [c16]Ivano Lauriola, Stefano Campese, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli:
Exploring the feature space of character-level embeddings. ESANN 2020: 637-642 - [c15]Ivano Lauriola, Fabio Aiolli:
Monotone Deep Spectrum Kernels. ICANN (1) 2020: 207-219 - [c14]Pasquale Capuozzo, Ivano Lauriola, Carlo Strapparava, Fabio Aiolli, Giuseppe Sartori:
DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text. LREC 2020: 1423-1430 - [i2]Ivano Lauriola, Alessandro Moschitti:
Context-based Transformer Models for Answer Sentence Selection. CoRR abs/2006.01285 (2020) - [i1]Ivano Lauriola, Fabio Aiolli:
MKLpy: a python-based framework for Multiple Kernel Learning. CoRR abs/2007.09982 (2020)
2010 – 2019
- 2019
- [c13]Agnieszka Kolasinska, Ivano Lauriola, Giacomo Quadrio:
Do people believe in Artificial Intelligence?: A cross-topic multicultural study. GOODTECHS 2019: 31-36 - [c12]Guglielmo Faggioli, Mirko Polato, Ivano Lauriola, Fabio Aiolli:
Evaluation of Tag Clusterings for User Profiling in Movie Recommendation. ICANN (Workshop) 2019: 456-468 - [c11]Mirko Polato, Guglielmo Faggioli, Ivano Lauriola, Fabio Aiolli:
Playing the Large Margin Preference Game. ICANN (2) 2019: 792-804 - [c10]Stefano Campese, Ivano Lauriola, Cristina Scarpazza, Giuseppe Sartori, Fabio Aiolli:
Psychiatric Disorders Classification with 3D Convolutional Neural Networks. INNSBDDL 2019: 48-57 - [c9]Ivano Lauriola, Mirko Polato, Guglielmo Faggioli, Fabio Aiolli:
A Preference-Learning Framework for Modeling Relational Data. INNSBDDL 2019: 359-369 - 2018
- [j1]Mirko Polato, Ivano Lauriola, Fabio Aiolli:
A Novel Boolean Kernels Family for Categorical Data. Entropy 20(6): 444 (2018) - [c8]Ivano Lauriola:
Learning Deep Representations for Natural Language Processing Applications. DC@AI*IA 2018: 16-20 - [c7]Ivano Lauriola, Riccardo Sella, Fabio Aiolli, Alberto Lavelli, Fabio Rinaldi:
Learning Representations for Biomedical NER. NL4AI@AI*IA 2018: 83-94 - [c6]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
The minimum effort maximum output principle applied to Multiple Kernel Learning. ESANN 2018 - [c5]Ivano Lauriola, Mirko Polato, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli:
Learning Preferences for Large Scale Multi-label Problems. ICANN (1) 2018: 546-555 - 2017
- [c4]Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa:
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN 2017 - [c3]Ivano Lauriola, Michele Donini, Fabio Aiolli:
Learning dot-product polynomials for multiclass problems. ESANN 2017 - [c2]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning. ICANN (2) 2017: 183-191 - [c1]Mirko Polato, Ivano Lauriola, Fabio Aiolli:
Classification of Categorical Data in the Feature Space of Monotone DNFs. ICANN (2) 2017: 279-286
Coauthor Index
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