Luciano Serafini

Artificial Intelligence · 2002–2020

A researcher in the Science corpus: 10 papers; trusts 387 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #11,816 of 35,345 by trust here, #11,072 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #11,219 of 21,575 by trust here, #13,815 by citations
• Natural Language Processing — 24 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Information Systems — 13 citations into it; #7,678 of 8,771 by trust here, #4,885 by citations
• Knowledge Graphs & Representation — 12 citations into it; #144 of 226 by trust here, #124 by citations
• Reinforcement Learning — 12 citations into it
• Aerospace Engineering — 11 citations into it

Whom Luciano Serafini trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ross B. Girshick
2. A.S. d'Avila Garcez
3. Jue Wang
4. Geoffrey E. Hinton
5. Richard Socher
6. Alex Krizhevsky
7. Pedro M. Domingos
8. Merrie Bergmann
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Luciano Serafini overall

1. A.S. d'Avila Garcez
2. Samy Badreddine
3. Ivan Donadello
4. Jaehun Lee
5. Marco Gori
6. Andres Campero
7. Michael Spranger
8. Gary F. Marcus
9. Francesco Giannini
10. Gesina Schwalbe

Papers from Luciano Serafini's perspective

Ordered by how strongly the researchers Luciano Serafini trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019) — 392 citations
• Logic Tensor Networks (2020) — 365 citations
• Exploiting Scene-specific Features for Object Goal Navigation (2020) — 41 citations
• Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation (2017) — 75 citations
• Logic Tensor Networks for Semantic Image Interpretation (2017) — 246 citations
• Tensor Networks : Deep Learning and Logical Reasoning from Data and Knowledge ⋆ (2016)
• Integration of numeric and symbolic information for semantic image interpretation (2016) — 16 citations
• C-OWL: Contextualizing Ontologies (2003) — 540 citations
• Distributed Description Logics: Assimilating Information from Peer Sources (2003) — 448 citations
• Data Management for Peer-to-Peer Computing : A Vision (2002) — 468 citations