Nikoli Dryden

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 3 papers; trusts 53 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #27,062 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 54 citations into it; #16,588 of 21,575 by trust here, #11,721 by citations
• Model Architectures — 30 citations into it
• Optimization & Training — 24 citations into it
• Efficiency & Compression — 16 citations into it; #602 of 1,264 by trust here, #633 by citations
• Hardware and Architecture — 15 citations into it

Whom Nikoli Dryden trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Yoshua Bengio
5. Alex Graves
6. Ilya Sutskever
7. Kaiming He
8. Diederik P. Kingma
9. Noam Shazeer
10. Sepp Hochreiter

Who trusts Nikoli Dryden overall

1. Yunhui Guo
2. Sebastian U. Stich
3. Alham Fikri Aji
4. Hao Yu
5. Samuel Horváth
6. Xu Sun
7. Chia-Yu Chen
8. Mingchao Yu
9. Kenneth Heafield
10. Dan Alistarh

Papers from Nikoli Dryden's perspective

Ordered by how strongly the researchers Nikoli Dryden 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Data Movement Is All You Need: A Case Study on Optimizing Transformers (2020) — 190 citations
• Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks (2021) — 986 citations
• Communication Quantization for Data-Parallel Training of Deep Neural Networks (2016) — 215 citations