Joongbo Shin

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 140 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it; #21,659 of 35,345 by trust here, #20,836 by citations
• Natural Language Processing — 79 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it; #13,473 of 21,575 by trust here, #20,271 by citations
• Evaluation & Benchmarks — 22 citations into it
• Information Retrieval & Search — 17 citations into it; #651 of 1,022 by trust here, #966 by citations
• Model Architectures — 13 citations into it
• Management Science and Operations Research — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Joongbo Shin trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Richard Socher
7. Sepp Hochreiter
8. Dzmitry Bahdanau
9. Jimmy Ba
10. Yann LeCun

Who trusts Joongbo Shin overall

1. Ye Jia
2. Songxiang Liu
3. Heeseung Kim
4. Jaehyeon Kim
5. Pushkar Mishra
6. Younghun Lee
7. Xu Tan
8. Michelle Tadmor Ramanovich
9. Ya-Zhen Song
10. Myeonghun Jeong

Papers from Joongbo Shin's perspective

Ordered by how strongly the researchers Joongbo Shin 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models (2022) — 137 citations
• Improving Neural Question Generation using Answer Separation (2018) — 175 citations
• Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (2021) — 42 citations
• Towards Continual Knowledge Learning of Language Models (2021) — 210 citations
• Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering (2017) — 35 citations