Maria Lomeli

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 6 papers; trusts 118 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 322 citations into it; #31,072 of 35,345 by trust here, #3,384 by citations
• Natural Language Processing — 181 citations into it
• Computer Vision and Pattern Recognition — 147 citations into it
• Information Retrieval & Search — 81 citations into it
• Prompting & Reasoning — 61 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Control and Systems Engineering — 10 citations into it
• Signal Processing — 10 citations into it; #6,620 of 7,572 by trust here, #7,324 by citations

Whom Maria Lomeli trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Ruslan Salakhutdinov
8. Yann LeCun
9. Alex Graves
10. Ronald J. Williams

Who trusts Maria Lomeli overall

1. Zhihong Shao
2. Zhibin Gou
3. Akari Asai
4. Lei Xu

Papers from Maria Lomeli's perspective

Ordered by how strongly the researchers Maria Lomeli 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Toolformer: Language Models Can Teach Themselves to Use Tools (2023) — 4,753 citations
• Few-shot Learning with Retrieval Augmented Language Models (2022) — 1,295 citations
• Augmented Language Models: a Survey (2023) — 579 citations
• Improving Wikipedia verifiability with AI (2022) — 23 citations
• General Latent Feature Models for Heterogeneous Datasets (2017) — 18 citations
• The Faiss Library (2024) — 768 citations