Andrew Tulloch

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 6 papers; trusts 75 researchers and is trusted by 1,078 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it; #3,620 of 35,345 by trust here, #1,399 by citations
• Computer Vision and Pattern Recognition — 130 citations into it; #1,314 of 21,575 by trust here, #1,049 by citations
• Optimization & Training — 43 citations into it; #708 of 2,601 by trust here, #142 by citations
• Model Architectures — 32 citations into it
• Hardware and Architecture — 25 citations into it
• Computer Networks and Communications — 13 citations into it

Whom Andrew Tulloch trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. Yann LeCun
6. Christian Szegedy
7. Karen Simonyan
8. Shaoqing Ren
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Andrew Tulloch overall

1. Yang You
2. Christoph Feichtenhofer
3. Kaiming He
4. Samuel L. Smith
5. Soham De
6. Xinlei Chen
7. Jiahui Yu
8. Yanghao Li
9. Haoqi Fan
10. Ross B. Girshick

Papers from Andrew Tulloch's perspective

Ordered by how strongly the researchers Andrew Tulloch 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Going deeper with convolutions
7. ImageNet Large Scale Visual Recognition Challenge
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Gradient-based learning applied to document recognition

Their papers

• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour (2017) — 4,148 citations
• GPT-4o System Card (2024) — 4,622 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• Machine Learning at Facebook: Understanding Inference at the Edge (2019) — 490 citations
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018) — 205 citations