Daniel Fink

Artificial Intelligence · with Liang · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Natural Language Processing — 18 citations into it
• Optimization & Training — 14 citations into it
• Reinforcement Learning — 5 citations into it

Whom Daniel Fink trusts overall

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

1. Geoffrey E. Hinton
2. Faustino J. Gomez
3. Diederik P. Kingma
4. Ronald J. Williams
5. Yoshua Bengio
6. Kaiming He
7. Alex Graves
8. Risto Miikkulainen
9. Tomas Mikolov
10. Alex Krizhevsky

Papers from Daniel Fink's perspective

Ordered by how strongly the researchers Daniel Fink 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Going deeper with convolutions
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Evolving Deep Neural Networks (2017) — 962 citations
• Evolutionary neural AutoML for deep learning (2019) — 120 citations