Jason Clemons

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 82 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it
• Artificial Intelligence — 62 citations into it; #27,300 of 35,345 by trust here, #20,836 by citations
• Efficiency & Compression — 28 citations into it
• Reinforcement Learning — 26 citations into it; #1,540 of 2,242 by trust here, #1,586 by citations
• Model Architectures — 16 citations into it
• Optimization & Training — 13 citations into it

Whom Jason Clemons trusts overall

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

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

Who trusts Jason Clemons overall

1. Steven Dalton
2. Yuxi Li

Papers from Jason Clemons's perspective

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

Their papers

• Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU (2016) — 297 citations
• vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design (2016) — 477 citations
• Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture (2019) — 495 citations
• Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design (2016) — 32 citations
• GA3C: GPU-based A3C for Deep Reinforcement Learning (2016) — 75 citations