Michael Gira

Artificial Intelligence · 2022

A researcher in the Science corpus: 2 papers; trusts 64 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it; #28,199 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 71 citations into it
• Large Language Models — 45 citations into it; #568 of 799 by trust here, #729 by citations
• Natural Language Processing — 45 citations into it
• Safety, Robustness & Interpretability — 25 citations into it; #1,815 of 2,251 by trust here, #2,205 by citations
• Safety Research — 10 citations into it
• Optimization & Training — 9 citations into it
• AI Ethics, Fairness & Society — 6 citations into it

Whom Michael Gira trusts overall

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

1. Jieyu Zhao
2. Alec Radford
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Alex Krizhevsky
7. Ilya Sutskever
8. Jacob Devlin
9. Yann LeCun
10. Kaiming He

Who trusts Michael Gira overall

1. Neeraja Kirtane
2. Stella Biderman
3. V. Manushree

Papers from Michael Gira's perspective

Ordered by how strongly the researchers Michael Gira 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022) — 200 citations
• Debiasing Pre-Trained Language Models via Efficient Fine-Tuning (2022) — 49 citations