Jakub Bartlomiej Swiatkowski

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Probabilistic & Bayesian Methods — 26 citations into it
• Optimization & Training — 18 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Statistics and Probability — 9 citations into it

Whom Jakub Bartlomiej Swiatkowski trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Xavier Glorot
5. Kaiming He
6. Yoshua Bengio
7. Radford M. Neal
8. Jimmy Ba
9. Yann LeCun
10. Ilya Sutskever

Who trusts Jakub Bartlomiej Swiatkowski overall

1. Moloud Abdar

Papers from Jakub Bartlomiej Swiatkowski's perspective

Ordered by how strongly the researchers Jakub Bartlomiej Swiatkowski 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. 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Discriminative k-shot learning using probabilistic models (2017) — 71 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