Tanel Pärnamaa

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 155 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #24,086 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #12,219 of 21,575 by trust here, #18,266 by citations
• Natural Language Processing — 41 citations into it; #4,018 of 5,688 by trust here, #3,235 by citations
• Molecular Biology — 33 citations into it; #6,603 of 17,773 by trust here, #5,764 by citations
• Model Architectures — 30 citations into it
• Optimization & Training — 23 citations into it
• Signal Processing — 17 citations into it
• Biophysics — 13 citations into it; #226 of 1,196 by trust here, #212 by citations

Whom Tanel Pärnamaa trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Alex Krizhevsky
5. Alex Graves
6. Yann LeCun
7. Diederik P. Kingma
8. Oriol Vinyals
9. Karen Simonyan
10. J. Urgen Schmidhuber

Who trusts Tanel Pärnamaa overall

1. Christof Angermueller
2. Mufti Mahmud
3. Erick Moen
4. Ben T Grys
5. Andrey Kan
6. Eirini Arvaniti
7. Qizhi He
8. M. S. Kaiser
9. Umut Eser
10. D. Bannon

Papers from Tanel Pärnamaa's perspective

Ordered by how strongly the researchers Tanel Pärnamaa 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Neural Knowledge Language Model (2016) — 124 citations
• Deep learning for computational biology (2016) — 1,263 citations
• ICASSP 2022 Acoustic Echo Cancellation Challenge (2022) — 81 citations
• Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning (2016) — 144 citations