Jeremy Barnes

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 68 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #28,139 of 35,345 by trust here, #31,361 by citations
• Natural Language Processing — 23 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Model Architectures — 6 citations into it

Whom Jeremy Barnes trusts overall

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

1. Geoffrey E. Hinton
2. Tomas Mikolov
3. Diederik P. Kingma
4. Richard Socher
5. Yoshua Bengio
6. Ilya Sutskever
7. Alex Krizhevsky
8. John Blitzer
9. Yann LeCun
10. Yoon Kim

Who trusts Jeremy Barnes overall

1. Magdalena Biesialska
2. Catherine Wong
3. Baiyun Cui
4. Katarzyna Biesialska
5. Neil Houlsby
6. Yingming Li

Papers from Jeremy Barnes's perspective

Ordered by how strongly the researchers Jeremy Barnes 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
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

• Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (2018) — 34 citations
• Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets (2017) — 75 citations