John Giorgi

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 201 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it; #31,701 of 35,345 by trust here, #17,110 by citations
• Natural Language Processing — 127 citations into it
• Large Language Models — 59 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it
• Representation & Self-Supervised Learning — 45 citations into it; #2,845 of 3,145 by trust here, #2,263 by citations
• Model Architectures — 18 citations into it
• Optimization & Training — 8 citations into it
• Signal Processing — 6 citations into it

Whom John Giorgi trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Kaiming He
4. Alex Graves
5. Alex Krizhevsky
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yann LeCun
9. R. Caruana
10. J. Urgen Schmidhuber

Who trusts John Giorgi overall

1. Niklas Muennighoff
2. Luyu Gao
3. Jinhyuk Lee
4. Tamara Czinczoll
5. Ashish Jaiswal
6. Rui Cheng
7. Bichen Wu
8. Ajinkya Tejankar
9. Yuanmeng Yan
10. WonJin Yoon

Papers from John Giorgi's perspective

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

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

Their papers

• DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations (2020) — 545 citations
• Transfer learning for biomedical named entity recognition with neural networks (2018) — 184 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations