Mart van Baalen

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 205 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #7,619 of 35,345 by trust here, #7,728 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #5,579 of 21,575 by trust here, #5,565 by citations
• Efficiency & Compression — 45 citations into it; #277 of 1,264 by trust here, #439 by citations
• Model Architectures — 16 citations into it
• Optimization & Training — 13 citations into it
• Statistical and Nonlinear Physics — 8 citations into it

Whom Mart van Baalen trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Christos Louizos
4. Geoffrey E. Hinton
5. Max Welling
6. Yoshua Bengio
7. Ronald J. Williams
8. Alex Krizhevsky
9. X. Zhang
10. Yann LeCun

Who trusts Mart van Baalen overall

1. Markus Nagel
2. Yelysei Bondarenko
3. Elias Frantar
4. Tijmen Blankevoort
5. Marios Fournarakis
6. Itay Hubara
7. Zi-Mei Yao
8. Yury Nahshan
9. Emilien Dupont
10. Tim Dettmers

Papers from Mart van Baalen's perspective

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

Their papers

• Data-Free Quantization Through Weight Equalization and Bias Correction (2019) — 645 citations
• Up or Down? Adaptive Rounding for Post-Training Quantization (2020) — 883 citations
• A White Paper on Neural Network Quantization (2021) — 894 citations
• Bayesian Bits: Unifying Quantization and Pruning (2020) — 143 citations