Nicola Tonellotto

Artificial Intelligence · with Nardini · 2013–2021

A researcher in the Science corpus: 6 papers; trusts 61 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #20,516 of 35,345 by trust here, #13,760 by citations
• Information Retrieval & Search — 66 citations into it
• Information Systems — 48 citations into it; #4,484 of 8,771 by trust here, #2,539 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #15,838 of 21,575 by trust here, #14,822 by citations
• Natural Language Processing — 25 citations into it
• Large Language Models — 17 citations into it
• Optimization & Training — 8 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Nicola Tonellotto trusts overall

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

1. Jerome H. Friedman
2. Rodrigo Nogueira
3. Zhuyun Dai
4. Diederik P. Kingma
5. Jimmy J. Lin
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Kyunghyun Cho
9. Jacob Devlin
10. Nick Craswell

Who trusts Nicola Tonellotto overall

1. Sebastian Bruch
2. Jimmy J. Lin
3. Joel M. Mackenzie
4. Sebastian Hofstätter
5. Thibault Formal
6. J. Shane Culpepper

Papers from Nicola Tonellotto's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Learning Passage Impacts for Inverted Indexes (2021) — 197 citations
• Efficient Document Re-Ranking for Transformers by Precomputing Term Representations (2020) — 132 citations
• Quality versus efficiency in document scoring with learning-to-rank models (2016) — 59 citations
• Expansion via Prediction of Importance with Contextualization (2020) — 122 citations
• Training Curricula for Open Domain Answer Re-Ranking (2020) — 20 citations
• Efficient and effective retrieval using selective pruning (2013) — 91 citations