Lukas Cavigelli

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 420 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 146 citations into it; #9,644 of 21,575 by trust here, #6,146 by citations
• Artificial Intelligence — 68 citations into it
• Efficiency & Compression — 40 citations into it; #1,065 of 1,264 by trust here, #685 by citations
• Media Technology — 20 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 20 citations into it
• Electrical and Electronic Engineering — 16 citations into it; #15,350 of 16,539 by trust here, #8,489 by citations
• Evaluation & Benchmarks — 14 citations into it

Whom Lukas Cavigelli trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Shaoqing Ren
8. Ronan Collobert
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Lukas Cavigelli overall

1. Yu-hsin Chen
2. Jiaxi Jiang
3. Vivienne Sze
4. Renzo Andri
5. Siwei Ma
6. Fabian Schuiki
7. Joel S. Emer
8. Kai Zhang
9. Kaiyuan Guo
10. Bert Moons

Papers from Lukas Cavigelli's perspective

Ordered by how strongly the researchers Lukas Cavigelli 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations (2017) — 535 citations
• CAS-CNN: A deep convolutional neural network for image compression artifact suppression (2016) — 216 citations
• YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration (2016) — 218 citations
• Origami: A Convolutional Network Accelerator (2015) — 157 citations
• EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces (2020) — 366 citations
• Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks (2017) — 46 citations
• YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights (2016) — 203 citations