Jeremy Howard

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 668 researchers and is trusted by 668 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #592 of 35,345 by trust here, #2,158 by citations
• Natural Language Processing — 68 citations into it; #215 of 5,688 by trust here, #485 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #7,879 of 21,575 by trust here, #15,529 by citations
• Representation & Self-Supervised Learning — 37 citations into it
• Model Architectures — 26 citations into it
• Optimization & Training — 25 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Information Systems — 11 citations into it

Whom Jeremy Howard trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ruslan Salakhutdinov
6. Ilya Sutskever
7. Yann LeCun
8. Diederik P. Kingma
9. Sergey Ioffe
10. Ross B. Girshick

Who trusts Jeremy Howard overall

1. Sandeep Attree
2. Alec Radford
3. Nitish Shirish Keskar
4. Quoc V. Le
5. Timo Schick
6. Matthew E. Peters
7. Sebastian Ruder
8. Minh-Thang Luong
9. Yu-An Chung
10. Samuel R. Bowman

Papers from Jeremy Howard's perspective

Ordered by how strongly the researchers Jeremy Howard 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. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Universal Language Model Fine-tuning for Text Classification (2018) — 4,208 citations
• fastai: A Layered API for Deep Learning (2020) — 1,032 citations
• Fine-tuned Language Models for Text Classification (2018) — 284 citations