David R. So
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 9 papers; trusts 543 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 417 citations into it; #14,155 of 35,345 by trust here, #2,301 by citations
- Computer Vision and Pattern Recognition — 193 citations into it; #12,239 of 21,575 by trust here, #2,718 by citations
- Natural Language Processing — 176 citations into it; #4,852 of 5,688 by trust here, #1,432 by citations
- Model Architectures — 96 citations into it; #620 of 2,278 by trust here, #721 by citations
- Evaluation & Benchmarks — 86 citations into it
- Large Language Models — 85 citations into it; #616 of 799 by trust here, #296 by citations
- Optimization & Training — 44 citations into it
- Efficiency & Compression — 32 citations into it
Whom David R. So trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Quoc V. Le
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
- Noam Shazeer
- Barret Zoph
- Kaiming He
- Jimmy Ba
Who trusts David R. So overall
- Yi Tay
- Emma Strubell
- David A. Patterson
- Quoc V. Le
- Derya Soydaner
- Sehoon Kim
- Mostafa Dehghani
- Chen Liang
- Adam Gaier
- Esteban Real
Papers from David R. So's perspective
Ordered by how strongly the researchers David R. So 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Carbon Emissions and Large Neural Network Training (2021) — 1,164 citations
- Towards a Human-like Open-Domain Chatbot (2020) — 1,033 citations
- PaLM 2 Technical Report (2023) — 1,524 citations
- Primer: Searching for Efficient Transformers for Language Modeling (2021) — 224 citations
- Pay Attention to MLPs (2021) — 883 citations
- Transcending Scaling Laws with 0.1% Extra Compute (2022) — 78 citations
- AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020) — 280 citations
- The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (2022) — 465 citations
- The Evolved Transformer (2019) — 499 citations