Bastiaan S. Veeling

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 8 papers; trusts 264 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #13,412 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #14,524 of 21,575 by trust here, #7,938 by citations
• Optimization & Training — 35 citations into it
• Model Architectures — 34 citations into it
• Representation & Self-Supervised Learning — 34 citations into it; #1,845 of 3,145 by trust here, #2,624 by citations
• Probabilistic & Bayesian Methods — 29 citations into it
• Cognitive Neuroscience — 19 citations into it
• Generative Models — 14 citations into it

Whom Bastiaan S. Veeling trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Jimmy Ba
7. Aapo Hyvärinen
8. Yann LeCun
9. Christian Szegedy
10. Max Welling

Who trusts Bastiaan S. Veeling overall

1. Erik J. Bekkers
2. David W. Romero
3. Daniel E. Worrall
4. Tianbao Yang
5. Eu Wern Teh
6. Ozan Ciga
7. Simon Graham
8. Max Welling
9. Geoffrey E. Hinton
10. Jingyi Zhang

Papers from Bastiaan S. Veeling's perspective

Ordered by how strongly the researchers Bastiaan S. Veeling 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Rotation Equivariant CNNs for Digital Pathology (2018) — 735 citations
• Putting An End to End-to-End: Gradient-Isolated Learning of Representations (2019) — 168 citations
• Hydra: Preserving Ensemble Diversity for Model Distillation (2020) — 65 citations
• Appendix for: How Good is the Bayes Posterior in Deep Neural Networks Really? (2020) — 424 citations
• The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks (2020) — 55 citations
• Scalable emulation of protein equilibrium ensembles with generative deep learning (2025) — 335 citations
• Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning (2019) — 5 citations
• Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks (2018) — 17 citations