Roberto Dessì
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 4 papers; trusts 77 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 218 citations into it; #21,520 of 35,345 by trust here, #4,526 by citations
- Natural Language Processing — 134 citations into it; #3,669 of 5,688 by trust here, #2,660 by citations
- Computer Vision and Pattern Recognition — 72 citations into it
- Model Architectures — 17 citations into it; #1,476 of 2,278 by trust here, #1,927 by citations
Whom Roberto Dessì trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Alex Graves
- Yann LeCun
- Kaiming He
- Ronald J. Williams
- Ashish Vaswani
Who trusts Roberto Dessì overall
- Jacob Russin
- Marco Baroni
- Zhibin Gou
- Jason Jo
- Geunwoo Kim
- Xingyao Wang
- Minghao Li
- Randall C. O’Reilly
- Zhihong Shao
Papers from Roberto Dessì's perspective
Ordered by how strongly the researchers Roberto Dessì trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Toolformer: Language Models Can Teach Themselves to Use Tools (2023) — 4,753 citations
- Augmented Language Models: a Survey (2023) — 579 citations
- CNNs found to jump around more skillfully than RNNs: Compositional Generalization in Seq2seq Convolutional Networks (2019) — 47 citations
- Enhancing Transformer for End-to-end Speech-to-Text Translation (2019) — 54 citations