Lukasz Kuci'nski

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 37 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it
• Reinforcement Learning — 29 citations into it
• Meta & Continual Learning — 28 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Optimization & Training — 8 citations into it
• Cognitive Neuroscience — 6 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Lukasz Kuci'nski trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Tuomas Haarnoja
6. Kaiming He
7. Yann LeCun
8. Richard S. Sutton
9. Ilya Sutskever
10. Jimmy Ba

Papers from Lukasz Kuci'nski's perspective

Ordered by how strongly the researchers Lukasz Kuci'nski 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Continual World: A Robotic Benchmark For Continual Reinforcement Learning (2021) — 138 citations
• Disentangling Transfer in Continual Reinforcement Learning (2022) — 42 citations