Horace He

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 95 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it
• Computer Vision and Pattern Recognition — 85 citations into it
• Safety, Robustness & Interpretability — 36 citations into it
• Model Architectures — 24 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Graph Learning — 20 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Molecular Biology — 7 citations into it

Whom Horace He trusts overall

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

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

Who trusts Horace He overall

1. Luke S. Zettlemoyer
2. Stella Biderman

Papers from Horace He's perspective

Ordered by how strongly the researchers Horace He 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022) — 1,029 citations
• Enhancing Adversarial Example Transferability With an Intermediate Level Attack (2019) — 300 citations
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (2020) — 328 citations
• Set-Structured Latent Representations (2020)
• Better Set Representations For Relational Reasoning (2020) — 22 citations
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling (2020) — 2,920 citations