Dehao Chen

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 9 papers; trusts 138 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 266 citations into it; #34,919 of 35,345 by trust here, #1,799 by citations
• Computer Vision and Pattern Recognition — 172 citations into it; #19,741 of 21,575 by trust here, #2,688 by citations
• Natural Language Processing — 114 citations into it
• Optimization & Training — 63 citations into it; #1,908 of 2,601 by trust here, #395 by citations
• Model Architectures — 54 citations into it
• Hardware and Architecture — 44 citations into it
• ML Systems & Hardware — 31 citations into it
• Statistical and Nonlinear Physics — 10 citations into it

Whom Dehao Chen trusts overall

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

1. Alex Krizhevsky
2. Noam Shazeer
3. Geoffrey E. Hinton
4. Kaiming He
5. Diederik P. Kingma
6. Ilya Sutskever
7. Yoshua Bengio
8. Ross B. Girshick
9. Ashish Vaswani
10. Yann LeCun

Who trusts Dehao Chen overall

1. Hieu Pham
2. Ankur Bapna
3. Samyam Rajbhandari
4. Jiahui Yu
5. Yuanzhong Xu
6. Yi Tay
7. Zihang Dai
8. Mingxing Tan
9. D. Narayanan
10. Barret Zoph

Papers from Dehao Chen's perspective

Ordered by how strongly the researchers Dehao Chen 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LaMDA: Language Models for Dialog Applications (2022) — 1,891 citations
• GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (2020) — 2,182 citations
• GSPMD: General and Scalable Parallelization for ML Computation Graphs (2021) — 188 citations
• Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
• Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models (2022) — 117 citations
• MLPerf Training Benchmark (2019) — 388 citations
• Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training (2020) — 52 citations
• Scale MLPerf-0.6 models on Google TPU-v3 Pods (2019) — 43 citations
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism (2018) — 2,195 citations