Chris Dongjoo Kim

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 959 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 210 citations into it; #18,090 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 203 citations into it; #7,100 of 21,575 by trust here, #5,565 by citations
• Representation & Self-Supervised Learning — 47 citations into it
• Optimization & Training — 42 citations into it
• Natural Language Processing — 39 citations into it
• Multimodal Learning — 38 citations into it
• Evaluation & Benchmarks — 33 citations into it
• Meta & Continual Learning — 32 citations into it; #549 of 1,041 by trust here, #613 by citations

Whom Chris Dongjoo Kim trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Jimmy Ba
7. Yann LeCun
8. Ilya Sutskever
9. Karen Simonyan
10. Ross B. Girshick

Who trusts Chris Dongjoo Kim overall

1. Matthias De Lange
2. Haiyan Yin
3. Zheda Mai
4. Liyuan Wang
5. Da-Wei Zhou
6. Benjamin Elizalde
7. Dongchao Yang
8. Quang Pham
9. Haohe Liu
10. Konstantinos Drossos

Papers from Chris Dongjoo Kim's perspective

Ordered by how strongly the researchers Chris Dongjoo Kim 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Generating images with recurrent adversarial networks (2016) — 227 citations
• AudioCaps: Generating Captions for Audios in The Wild (2019) — 866 citations
• Video Question Answering with Spatio-Temporal Reasoning (2019) — 68 citations
• Generative Adversarial Parallelization (2016) — 40 citations
• Continual Learning on Noisy Data Streams via Self-Purified Replay (2021) — 58 citations
• Imbalanced Continual Learning with Partitioning Reservoir Sampling (2020) — 136 citations