Jerret Ross

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 91 researchers and is trusted by 117 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #4,359 of 35,345 by trust here, #4,137 by citations
• Computational Theory and Mathematics — 53 citations into it
• Materials Chemistry — 50 citations into it
• Molecular Biology — 44 citations into it
• Computer Vision and Pattern Recognition — 42 citations into it; #3,080 of 21,575 by trust here, #3,191 by citations
• Graph Learning — 28 citations into it
• Natural Language Processing — 25 citations into it; #1,127 of 5,688 by trust here, #1,024 by citations
• Evaluation & Benchmarks — 13 citations into it

Whom Jerret Ross trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Oriol Vinyals
6. Jacob Devlin
7. Kaiming He
8. Ilya Sutskever
9. Yann LeCun
10. Jimmy Ba

Who trusts Jerret Ross overall

1. Marcella Cornia
2. Mohit Bansal
3. Ruotian Luo
4. Ramakanth Pasunuru
5. Lorenzo Baraldi
6. Jiuxiang Gu
7. Peter Anderson
8. Ting Yao
9. Matteo Stefanini
10. Huan Ling

Papers from Jerret Ross's perspective

Ordered by how strongly the researchers Jerret Ross 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Self-Critical Sequence Training for Image Captioning (2016) — 2,119 citations
• Do Large Scale Molecular Language Representations Capture Important Structural Information? (2021) — 13 citations
• Large-scale chemical language representations capture molecular structure and properties (2021) — 591 citations
• Wasserstein Barycenter Model Ensembling (2019) — 27 citations