Pasquale Minervini
Citado por
Citado por
Convolutional 2d knowledge graph embeddings
T Dettmers, P Minervini, P Stenetorp, S Riedel
AAAI 2017, 2017
Adversarial sets for regularising neural link predictors
P Minervini, T Demeester, T Rocktäschel, S Riedel
UAI 2017, 2017
Adversarially regularising neural nli models to integrate logical background knowledge
P Minervini, S Riedel
CoNLL 2018, 2018
Differentiable Reasoning on Large Knowledge Bases and Natural Language
P Minervini, M Bosnjak, T Rocktäschel, S Riedel, E Grefenstette
AAAI 2020 (Oral), 125-142, 2020
Nlprolog: Reasoning with weak unification for question answering in natural language
L Weber, P Minervini, J Münchmeyer, U Leser, T Rocktäschel
ACL 2019, 2019
Regularizing knowledge graph embeddings via equivalence and inversion axioms
P Minervini, L Costabello, E Muñoz, V Nováček, PY Vandenbussche
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
Towards neural theorem proving at scale
P Minervini, M Bosnjak, T Rocktäschel, S Riedel
arXiv preprint arXiv:1807.08204, 2018
Can real-time machine translation overcome language barriers in distributed requirements engineering?
F Calefato, F Lanubile, P Minervini
2010 5th IEEE International Conference on Global Software Engineering, 257-264, 2010
Knowledge Graph Embeddings and Explainable AI
F Bianchi, G Rossiello, L Costabello, M Palmonari, P Minervini
IOS Press, 2020
Make up your mind! Adversarial generation of inconsistent natural language explanations
OM Camburu, B Shillingford, P Minervini, T Lukasiewicz, P Blunsom
ACL 2019, 2019
Leveraging the schema in latent factor models for knowledge graph completion
P Minervini, C d'Amato, N Fanizzi, F Esposito
Proceedings of the 31st annual ACM symposium on applied computing, 327-332, 2016
Scalable learning of entity and predicate embeddings for knowledge graph completion
P Minervini, N Fanizzi, C d'Amato, F Esposito
2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015
NeurIPS 2020 EfficientQA competition: Systems, analyses and lessons learned
S Min, J Boyd-Graber, C Alberti, D Chen, E Choi, M Collins, K Guu, ...
arXiv preprint arXiv:2101.00133, 2021
Extrapolation in NLP
J Mitchell, P Minervini, P Stenetorp, S Riedel
arXiv preprint arXiv:1805.06648, 2018
Learning Reasoning Strategies in End-to-End Differentiable Proving
P Minervini, S Riedel, P Stenetorp, E Grefenstette, T Rocktäschel
ICML 2020, 2020
Efficient energy-based embedding models for link prediction in knowledge graphs
P Minervini, C d’Amato, N Fanizzi
Journal of Intelligent Information Systems 47 (1), 91-109, 2016
Avoiding the hypothesis-only bias in natural language inference via ensemble adversarial training
J Stacey, P Minervini, H Dubossarsky, S Riedel, T Rocktäschel
arXiv preprint arXiv:2004.07790, 2020
Complex Query Answering with Neural Link Predictors
E Arakelyan, D Daza, P Minervini, M Cochez
ICLR 2021 (Oral, Outstanding Paper Award), 2020
Adaptive knowledge propagation in web ontologies
P Minervini, V Tresp, C D'amato, N Fanizzi
ACM Transactions on the Web (TWEB) 12 (1), 1-28, 2017
Numeric prediction on OWL knowledge bases through terminological regression trees
N Fanizzi, C d'Amato, F Esposito, P Minervini
International Journal of Semantic Computing 6 (04), 429-446, 2012
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