Jeffrey Ling

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 217 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #10,899 of 35,345 by trust here, #6,468 by citations
• Natural Language Processing — 66 citations into it; #2,235 of 5,688 by trust here, #1,598 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #9,661 of 21,575 by trust here, #7,499 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Large Language Models — 6 citations into it
• Information Systems — 5 citations into it

Whom Jeffrey Ling trusts overall

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

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

Who trusts Jeffrey Ling overall

1. Zhe Gan
2. Linjie Li
3. Yen-Chun Chen
4. Xu Han
5. Zhicheng Huang
6. Amanpreet Singh
7. Ikuya Yamada
8. Tara Safavi
9. Zhihan Zhang
10. Haitian Sun

Papers from Jeffrey Ling's perspective

Ordered by how strongly the researchers Jeffrey Ling 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Fusion of Detected Objects in Text for Visual Question Answering (2019) — 185 citations
• Matching the Blanks: Distributional Similarity for Relation Learning (2019) — 866 citations
• Image-to-Markup Generation with Coarse-to-Fine Attention (2016) — 276 citations
• Learning Cross-Context Entity Representations from Text (2020) — 33 citations