Byeongchang Kim
Artificial Intelligence · 2017–2026
A researcher in the Science corpus: 5 papers; trusts 522 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 112 citations into it; #15,265 of 35,345 by trust here, #11,750 by citations
- Natural Language Processing — 87 citations into it; #2,995 of 5,688 by trust here, #3,014 by citations
- Computer Vision and Pattern Recognition — 77 citations into it; #13,559 of 21,575 by trust here, #9,305 by citations
- Evaluation & Benchmarks — 29 citations into it
- Multimodal Learning — 27 citations into it
- Signal Processing — 26 citations into it; #4,465 of 7,572 by trust here, #2,816 by citations
- Model Architectures — 21 citations into it
- Representation & Self-Supervised Learning — 16 citations into it
Whom Byeongchang Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Jimmy Ba
- Ilya Sutskever
- Alex Krizhevsky
- Alex Graves
- Kaiming He
- Oriol Vinyals
- Jason Aaron Edward Weston
Who trusts Byeongchang Kim overall
- Wen Zheng
- Haolan Zhan
- Andrew Hoang
- David Thulke
- Zi-Yi Dou
- Ashwin Paranjape
- Rahul Aralikatte
- Timo Schick
- Xueliang Zhao
- Jesse Dodge
Papers from Byeongchang Kim's perspective
Ordered by how strongly the researchers Byeongchang Kim trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- AudioCaps: Generating Captions for Audios in The Wild (2019) — 866 citations
- Attend to You: Personalized Image Captioning with Context Sequence Memory Networks (2017) — 178 citations
- Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (2018) — 205 citations
- Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue (2020) — 181 citations
- Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform (2026)