Matteo Pagliardini

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 2 papers; trusts 71 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #9,449 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 45 citations into it; #1,913 of 5,688 by trust here, #3,190 by citations
• Representation & Self-Supervised Learning — 19 citations into it; #1,146 of 3,145 by trust here, #1,885 by citations
• Evaluation & Benchmarks — 14 citations into it

Whom Matteo Pagliardini trusts overall

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

1. Tomas Mikolov
2. Yoshua Bengio
3. Kyunghyun Cho
4. Geoffrey E. Hinton
5. Bo Pang
6. Quoc V. Le
7. Diederik P. Kingma
8. Ilya Sutskever
9. Alex Krizhevsky
10. Christopher D. Manning

Who trusts Matteo Pagliardini overall

1. John Wieting
2. David M. Beskow
3. Nikunj Saunshi
4. Ziqi Wang
5. Sanjeev Arora
6. Dhivya Chandrasekaran
7. Mikhail Khodak
8. Ziyi Yang
9. Arun Tejasvi Chaganty
10. V. Venktesh

Papers from Matteo Pagliardini's perspective

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

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

• Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (2017) — 737 citations
• MEDITRON-70B: Scaling Medical Pretraining for Large Language Models (2023) — 399 citations