Aaron Harlap

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 126 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #27,002 of 35,345 by trust here, #8,210 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #16,552 of 21,575 by trust here, #5,890 by citations
• Optimization & Training — 54 citations into it; #2,164 of 2,601 by trust here, #759 by citations
• ML Systems & Hardware — 29 citations into it
• Computer Networks and Communications — 27 citations into it
• Hardware and Architecture — 18 citations into it
• Information Systems — 16 citations into it
• Model Architectures — 16 citations into it

Whom Aaron Harlap trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Diederik P. Kingma
5. Sergey Ioffe
6. Kaiming He
7. Henggang Cui
8. Ilya Sutskever
9. Yoshua Bengio
10. Yann LeCun

Who trusts Aaron Harlap overall

1. Samyam Rajbhandari
2. D. Narayanan
3. Kevin Hsieh
4. Chi-Chung Chen
5. Zhuohan Li
6. Jeff Rasley
7. Amar Phanishayee
8. Md Maruf Hossain Shuvo
9. Yanping Huang
10. Reza Yazdani Aminabadi

Papers from Aaron Harlap's perspective

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

Their papers

• PipeDream: generalized pipeline parallelism for DNN training (2019) — 1,228 citations
• PipeDream: Fast and Efficient Pipeline Parallel DNN Training (2018) — 317 citations
• PipeDream: Pipeline Parallelism for DNN Training (2018)
• Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds (2017) — 450 citations
• Addressing the straggler problem for iterative convergent parallel ML (2016) — 148 citations