Jason Lowe-Power

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 14 citations into it
• Model Architectures — 7 citations into it

Whom Jason Lowe-Power trusts overall

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

1. Karen Simonyan
2. Yann LeCun
3. Kaiming He
4. Paul Rosenfeld
5. Yoongu Kim
6. Scott Beamer
7. Alexander Schrijver
8. Milo M. K. Martin
9. Christian Szegedy

Papers from Jason Lowe-Power's perspective

Ordered by how strongly the researchers Jason Lowe-Power 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. Gradient-based learning applied to document recognition
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Densely Connected Convolutional Networks

Their papers

• AutoTM: Automatic Tensor Movement in Heterogeneous Memory Systems using Integer Linear Programming (2020) — 91 citations
• The gem5 Simulator: Version 20.0+ (2020) — 425 citations