Mi-Young Kim
Natural Language Processing · with Goebel · 2016–2019
A researcher in the Science corpus: 5 papers; trusts 52 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 34 citations into it; #4,553 of 5,688 by trust here, #5,502 by citations
- Artificial Intelligence — 33 citations into it; #16,369 of 35,345 by trust here, #25,809 by citations
- Evaluation & Benchmarks — 12 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Information Retrieval & Search — 6 citations into it
Whom Mi-Young Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tomas Mikolov
- Quoc V. Le
- Karen Spärck Jones
- Samuel R. Bowman
- Ion Androutsopoulos
- Ilya Sutskever
- Geoffrey E. Hinton
- Sepp Hochreiter
- Yoshua Bengio
- Jacob Devlin
Who trusts Mi-Young Kim overall
- Juliano Rabelo
- Jimmy J. Lin
- Randy Goebel
- Rodrigo Nogueira
- Yoshinobu Kano
- Andrew Yates
- Masaharu Yoshioka
Papers from Mi-Young Kim's perspective
Ordered by how strongly the researchers Mi-Young Kim trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Combining Similarity and Transformer Methods for Case Law Entailment (2019) — 31 citations
- A Summary of the COLIEE 2019 Competition (2019) — 85 citations
- Statute Law Information Retrieval and Entailment (2019) — 34 citations
- Overview of COLIEE 2017 (2017) — 26 citations
- COLIEE-2016: Evaluation of the Competition on Legal Information Extraction and Entailment (2016)