Seohyun Kim

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #18,943 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 55 citations into it; #3,645 of 5,688 by trust here, #4,032 by citations
• Information Systems — 52 citations into it; #3,035 of 8,771 by trust here, #2,406 by citations
• Code Generation & Program Synthesis — 24 citations into it; #87 of 220 by trust here, #72 by citations
• Software — 21 citations into it; #622 of 1,227 by trust here, #534 by citations
• Signal Processing — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 9 citations into it

Whom Seohyun Kim trusts overall

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

1. Uri Alon
2. Veselin Raychev
3. Premkumar T. Devanbu
4. Sepp Hochreiter
5. Yoshua Bengio
6. Pavol Bielik
7. Miltiadis Allamanis
8. Geoffrey E. Hinton
9. Oriol Vinyals
10. Karen Simonyan

Who trusts Seohyun Kim overall

1. Baptiste Rozière
2. Maliheh Izadi
3. Matteo Ciniselli
4. Gareth Ari Aye
5. F. Liu
6. Max Schäfer
7. Ge Li
8. Han Peng
9. Roberta Gismondi
10. Marie-Anne Lachaux

Papers from Seohyun Kim's perspective

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

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

Their papers

• Code Prediction by Feeding Trees to Transformers (2020) — 241 citations
• 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