Ladislav Rampášek

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 50 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 — 40 citations into it
• Materials Chemistry — 34 citations into it
• Graph Learning — 24 citations into it
• Model Architectures — 19 citations into it
• Statistical and Nonlinear Physics — 11 citations into it
• Generative Models — 9 citations into it
• Biophysics — 8 citations into it

Whom Ladislav Rampášek trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Vijay Prakash Dwivedi
4. Yoshua Bengio
5. Weihua Hu
6. Alex Krizhevsky
7. Ronald J. Williams
8. Xavier Bresson
9. Ilya Sutskever
10. Yann LeCun

Who trusts Ladislav Rampášek overall

1. Bowen Jin

Papers from Ladislav Rampášek's perspective

Ordered by how strongly the researchers Ladislav Rampášek 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Long Range Graph Benchmark (2022) — 345 citations
• Dropout Feature Ranking for Deep Learning Models (2017) — 53 citations
• Recipe for a General, Powerful, Scalable Graph Transformer (2022) — 1,075 citations
• Machine learning approaches to drug response prediction: challenges and recent progress (2020) — 326 citations