Hongyu Li

Artificial Intelligence · with Kim · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Natural Language Processing — 31 citations into it
• Information Systems — 29 citations into it
• Code Generation & Program Synthesis — 16 citations into it
• Software — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Hongyu Li trusts overall

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

1. Yoshua Bengio
2. Tomas Mikolov
3. Kenneth Heafield
4. Miltiadis Allamanis
5. Ilya Sutskever
6. Premkumar T. Devanbu
7. Sepp Hochreiter
8. Geoffrey E. Hinton
9. Piotr Bojanowski
10. Diederik P. Kingma

Who trusts Hongyu Li overall

1. Noam Yefet
2. Varot Premtoon
3. Michael Pradel
4. Aditya Kanade
5. Baptiste Rozière

Papers from Hongyu Li's perspective

Ordered by how strongly the researchers Hongyu Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• When deep learning met code search (2019) — 239 citations
• Learning Autocompletion from Real-World Datasets (2020) — 37 citations
• Retrieval on source code: a neural code search (2018) — 200 citations