Tommaso Di Noia

Information Systems · 2012–2018

A researcher in the Science corpus: 6 papers; trusts 43 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 72 citations into it; #3,572 of 8,771 by trust here, #4,341 by citations
• Artificial Intelligence — 51 citations into it; #17,711 of 35,345 by trust here, #20,836 by citations
• Recommender Systems — 30 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Management Science and Operations Research — 18 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Marketing — 7 citations into it
• Signal Processing — 7 citations into it

Whom Tommaso Di Noia trusts overall

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

1. Christian Bizer
2. Geoffrey E. Hinton
3. Gerard A. Salton
4. Steffen Rendle
5. Yoshua Bengio
6. Jonathan L. Herlocker
7. Zeno Gantner
8. Ricardo A. Baeza-Yates
9. Xavier Glorot
10. Nava Tintarev

Who trusts Tommaso Di Noia overall

1. Vito Bellini
2. Sarasi Lalithsena
3. Petar Ristoski
4. Angelo Schiavone
5. Azzurra Ragone
6. Heiko Paulheim
7. Eugenio Di Sciascio

Papers from Tommaso Di Noia's perspective

Ordered by how strongly the researchers Tommaso Di Noia 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018) — 8 citations
• Knowledge-aware Autoencoders for Explainable Recommender Systems (2018) — 45 citations
• Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios (2017) — 31 citations
• Schema-summarization in linked-data-based feature selection for recommender systems (2017) — 24 citations
• Sound and Music Recommendation with Knowledge Graphs (2016) — 152 citations
• Linked open data to support content-based recommender systems (2012) — 312 citations