Przemyslaw Spurek

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 57 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Safety, Robustness & Interpretability — 23 citations into it
• 3D Vision & Pose — 13 citations into it
• Electrical and Electronic Engineering — 8 citations into it
• Generative Models — 8 citations into it

Whom Przemyslaw Spurek trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoon Kim
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Kaiming He
9. Max Welling
10. Thomas Kipf

Who trusts Przemyslaw Spurek overall

1. Magdalena Proszewska

Papers from Przemyslaw Spurek's perspective

Ordered by how strongly the researchers Przemyslaw Spurek 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. 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. 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

• Spatial Graph Convolutional Networks (2020) — 115 citations
• Continual Learning with Guarantees via Weight Interval Constraints (2022) — 13 citations
• HyperCube: Implicit Field Representations of Voxelized 3D Models (2021) — 5 citations
• Geometric Graph Convolutional Neural Networks (2019) — 10 citations
• Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models (2019) — 9 citations