Aleksander Smywiński-Pohl

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 44 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it
• Natural Language Processing — 21 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Aleksander Smywiński-Pohl trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Matthew D. Zeiler
6. Paul J. Werbos
7. Alex Krizhevsky
8. George A. Miller
9. Ilya Sutskever
10. Yoon Kim

Papers from Aleksander Smywiński-Pohl's perspective

Ordered by how strongly the researchers Aleksander Smywiński-Pohl 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Towards textual data augmentation for neural networks: synonyms and maximum loss (2019) — 31 citations