Jeff Pool

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 7 papers; trusts 297 researchers and is trusted by 799 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #1,556 of 21,575 by trust here, #1,055 by citations
• Artificial Intelligence — 109 citations into it; #1,491 of 35,345 by trust here, #1,424 by citations
• Efficiency & Compression — 77 citations into it; #37 of 1,264 by trust here, #36 by citations
• Model Architectures — 42 citations into it
• Optimization & Training — 27 citations into it
• Computer Networks and Communications — 11 citations into it
• Media Technology — 7 citations into it

Whom Jeff Pool trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Karen Simonyan
5. Kaiming He
6. Yoshua Bengio
7. Christian Szegedy
8. Ilya Sutskever
9. Andrew Zisserman
10. Song Han

Who trusts Jeff Pool overall

1. Song Han
2. Yang He
3. Ji Lin
4. Jonathan Frankle
5. Han Cai
6. Arun Mallya
7. Zhaowei Cai
8. Yunhe Wang
9. Karim Ahmed
10. Wujie Wen

Papers from Jeff Pool's perspective

Ordered by how strongly the researchers Jeff Pool 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. Gradient-based learning applied to document recognition
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. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning both Weights and Connections for Efficient Neural Network (2015) — 7,826 citations
• DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow (2016) — 83 citations
• Exploring the Regularity of Sparse Structure in Convolutional Neural Networks (2017) — 258 citations
• Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks (2017) — 193 citations
• Accelerating Sparse Deep Neural Networks (2021) — 344 citations
• Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs (2019) — 65 citations
• DSD: Dense-Sparse-Dense Training for Deep Neural Networks (2016) — 219 citations