Gabriel Recchia

Artificial Intelligence · with Bowman · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 121 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #12,736 of 35,345 by trust here, #25,809 by citations
• Large Language Models — 29 citations into it; #256 of 799 by trust here, #770 by citations
• Natural Language Processing — 27 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Reinforcement Learning — 12 citations into it
• Safety, Robustness & Interpretability — 8 citations into it

Whom Gabriel Recchia trusts overall

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

1. Alexander J. Ratner
2. Guillaume Lample
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Michael Janner
8. Faraz Torabi
9. Ilya Sutskever
10. Kaiming He

Who trusts Gabriel Recchia overall

1. Noam Wies
2. Jingu Qian
3. Yoav Levine
4. Hong Wang
5. Amnon Shashua
6. Andrew Kyle Lampinen
7. Zekun Li
8. Adam Santoro
9. SHIYANG LI

Papers from Gabriel Recchia's perspective

Ordered by how strongly the researchers Gabriel Recchia 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
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. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Inverse Scaling: When Bigger Isn't Better (2023) — 235 citations
• Teaching Autoregressive Language Models Complex Tasks By Demonstration (2021) — 23 citations