Markus Nagel

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 8 papers; trusts 522 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it; #5,698 of 35,345 by trust here, #6,103 by citations
• Computer Vision and Pattern Recognition — 160 citations into it; #4,656 of 21,575 by trust here, #4,623 by citations
• Efficiency & Compression — 121 citations into it; #218 of 1,264 by trust here, #372 by citations
• Model Architectures — 44 citations into it
• Optimization & Training — 26 citations into it
• Statistical and Nonlinear Physics — 15 citations into it
• Electrical and Electronic Engineering — 13 citations into it
• Natural Language Processing — 12 citations into it

Whom Markus Nagel trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Diederik P. Kingma
4. Raghuraman Krishnamoorthi
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Yann LeCun
8. X. Zhang
9. Max Welling
10. Ilya Sutskever

Who trusts Markus Nagel overall

1. Elias Frantar
2. Yelysei Bondarenko
3. Tijmen Blankevoort
4. Mart van Baalen
5. Marios Fournarakis
6. Itay Hubara
7. Zi-Mei Yao
8. Dan Alistarh
9. Yury Nahshan
10. Ji Lin

Papers from Markus Nagel's perspective

Ordered by how strongly the researchers Markus Nagel 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Data-Free Quantization Through Weight Equalization and Bias Correction (2019) — 645 citations
• Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021) — 196 citations
• Up or Down? Adaptive Rounding for Post-Training Quantization (2020) — 883 citations
• LSQ+: Improving low-bit quantization through learnable offsets and better initialization (2020) — 310 citations
• Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks (2019) — 27 citations
• Overcoming Oscillations in Quantization-Aware Training (2022) — 171 citations
• A White Paper on Neural Network Quantization (2021) — 894 citations
• Bayesian Bits: Unifying Quantization and Pruning (2020) — 143 citations