Howard Chen

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 195 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #19,922 of 35,345 by trust here, #8,365 by citations
• Natural Language Processing — 74 citations into it; #3,399 of 5,688 by trust here, #1,891 by citations
• Computer Vision and Pattern Recognition — 65 citations into it; #12,437 of 21,575 by trust here, #5,987 by citations
• Reinforcement Learning — 39 citations into it
• Model Architectures — 15 citations into it
• Large Language Models — 13 citations into it
• Management Science and Operations Research — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Howard Chen trusts overall

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

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

Who trusts Howard Chen overall

1. Khanh Nguyen
2. Alexander Ku
3. Vihan Jain
4. Yicong Hong
5. Harsh Mehta
6. Gabriel Ilharco
7. Eugene Ie
8. Geoffrey Cideron
9. Peter Anderson
10. Jacob Krantz

Papers from Howard Chen's perspective

Ordered by how strongly the researchers Howard 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. 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments (2018) — 471 citations
• WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (2022) — 1,205 citations
• Autoregressive Knowledge Distillation through Imitation Learning (2020) — 72 citations