Da Yu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Optimization & Training — 18 citations into it; #2,258 of 2,601 by trust here, #2,519 by citations
• Model Architectures — 11 citations into it
• Scaling & Learning Theory — 10 citations into it; #349 of 428 by trust here, #417 by citations
• Computational Mechanics — 7 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Da Yu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Sepp Hochreiter
9. Christian Szegedy
10. J. Urgen Schmidhuber

Who trusts Da Yu overall

1. Difan Zou
2. Tom Sander
3. Soham De
4. Justus Mattern
5. Natalia Ponomareva

Papers from Da Yu's perspective

Ordered by how strongly the researchers Da Yu 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. 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

• Differentially Private Fine-tuning of Language Models (2021) — 517 citations
• Training Over-parameterized Deep ResNet Is almost as Easy as Training a Two-layer Network (2019) — 15 citations