Jacek Tabor

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 38 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it
• Computational Theory and Mathematics — 48 citations into it
• Optimization & Training — 27 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Natural Language Processing — 7 citations into it

Whom Jacek Tabor trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Ian J. Goodfellow
9. Oriol Vinyals
10. Christian Szegedy

Papers from Jacek Tabor's perspective

Ordered by how strongly the researchers Jacek Tabor 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The Break-Even Point on Optimization Trajectories of Deep Neural Networks (2020) — 203 citations
• Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening (2020) — 26 citations
• Spatial Graph Convolutional Networks (2020) — 115 citations
• Continual Learning with Guarantees via Weight Interval Constraints (2022) — 13 citations
• Geometric Graph Convolutional Neural Networks (2019) — 10 citations
• Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models (2019) — 9 citations