M. Paul

Artificial Intelligence · with Biderman · 2021–2024

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Optimization & Training — 9 citations into it
• Scaling & Learning Theory — 7 citations into it
• Model Architectures — 6 citations into it

Whom M. Paul trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Chiyuan Zhang
6. Arthur Jacot
7. X. Zhang
8. Vitaly Feldman
9. Diederik P. Kingma
10. Yann LeCun

Who trusts M. Paul overall

1. Ben Sorscher
2. Shoaib Ahmed Siddiqui
3. Ahmet Üstün
4. Nitarshan Rajkumar
5. Robert Geirhos

Papers from M. Paul's perspective

Ordered by how strongly the researchers M. Paul 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Learning Multiple Layers of Features from Tiny Images
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
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

• Deep Learning on a Data Diet: Finding Important Examples Early in Training (2021) — 744 citations
• LoRA Learns Less and Forgets Less (2024) — 354 citations