Patrick Judd

Computer Vision and Pattern Recognition · 2015–2023

A researcher in the Science corpus: 5 papers; trusts 125 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 70 citations into it; #12,366 of 21,575 by trust here, #8,722 by citations
• Artificial Intelligence — 60 citations into it; #19,787 of 35,345 by trust here, #12,580 by citations
• Efficiency & Compression — 44 citations into it; #932 of 1,264 by trust here, #987 by citations
• Model Architectures — 24 citations into it
• Optimization & Training — 15 citations into it
• ML Systems & Hardware — 14 citations into it
• Hardware and Architecture — 11 citations into it; #985 of 1,551 by trust here, #692 by citations
• Electrical and Electronic Engineering — 10 citations into it

Whom Patrick Judd trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Matthieu Courbariaux
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Yann LeCun
7. Kaiming He
8. Ilya Sutskever
9. Ross B. Girshick
10. Kyuyeon Hwang

Who trusts Patrick Judd overall

1. Eunhyeok Park
2. Tim Dettmers
3. By Lei Deng
4. Animesh Jain
5. Ahmed T. Elthakeb
6. Udit Gupta
7. Chaim Baskin
8. Hardik Sharma
9. Md Maruf Hossain Shuvo
10. Itay Hubara

Papers from Patrick Judd's perspective

Ordered by how strongly the researchers Patrick Judd 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation (2020) — 488 citations
• FP8 Formats for Deep Learning (2022) — 285 citations
• Stripes: Bit-serial deep neural network computing (2016) — 413 citations
• Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets (2015) — 103 citations
• RETROSPECTIVE: Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing (2023)