Xingyou Song
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 5 papers; trusts 51 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 135 citations into it; #18,867 of 35,345 by trust here, #4,526 by citations
- Computer Vision and Pattern Recognition — 65 citations into it; #15,847 of 21,575 by trust here, #3,875 by citations
- Model Architectures — 59 citations into it; #1,683 of 2,278 by trust here, #506 by citations
- Natural Language Processing — 40 citations into it
- Efficiency & Compression — 24 citations into it
- Molecular Biology — 20 citations into it
- Scaling & Learning Theory — 16 citations into it
- Computational Biology & Bioinformatics — 15 citations into it
Whom Xingyou Song trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David A. McAllester
- Behnam Neyshabur
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Yann LeCun
- Ilya Sutskever
- Peter L. Bartlett
- Kaiming He
Who trusts Xingyou Song overall
- Tri Dao
- Rishabh Agarwal
- Yi Tay
- Roberta Raileanu
- A. Ozbayoglu
- Omer Berat Sezer
- M. Ugur Gudelek
- Sae-Young Chung
- Richard Ngo
- Marlos C. Machado
Papers from Xingyou Song's perspective
Ordered by how strongly the researchers Xingyou Song trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Rethinking Attention with Performers (2020) — 2,365 citations
- Observational Overfitting in Reinforcement Learning (2019) — 150 citations
- Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers (2020) — 96 citations
- Sub-Linear Memory: How to Make Performers SLiM (2020) — 25 citations
- Sentiment Predictability for Stocks (2017) — 15 citations