Hyungjin Chung
Computer Vision and Pattern Recognition · 2021–2022
A researcher in the Science corpus: 4 papers; trusts 449 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 91 citations into it
- Generative Models — 76 citations into it
- Artificial Intelligence — 33 citations into it
- Statistical and Nonlinear Physics — 27 citations into it; #1,703 of 10,127 by trust here, #3,382 by citations
- Probabilistic & Bayesian Methods — 19 citations into it
- Signal Processing — 16 citations into it
- Radiology, Nuclear Medicine and Imaging — 14 citations into it; #313 of 3,265 by trust here, #1,332 by citations
- Computational Mechanics — 12 citations into it
Whom Hyungjin Chung trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yang Song
- Diederik P. Kingma
- Geoffrey E. Hinton
- Prafulla Dhariwal
- Stefano Ermon
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Alex Nichol
- Yann LeCun
Who trusts Hyungjin Chung overall
- Amirhossein Kazerouni
- Hanqun Cao
- Kai Zhang
- Jong Chul Ye
- Jeongsol Kim
- Ehsan Khodapanah Aghdam
- Cheng Tan
- Zongsheng Yue
- Byeongsu Sim
- Zhijun Liu
Papers from Hyungjin Chung's perspective
Ordered by how strongly the researchers Hyungjin Chung 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- GENERATIVE ADVERSARIAL NETS
Their papers
- Improving Diffusion Models for Inverse Problems using Manifold Constraints (2022) — 698 citations
- Diffusion Posterior Sampling for General Noisy Inverse Problems (2022) — 1,760 citations
- Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction (2021) — 468 citations
- Score-based diffusion models for accelerated MRI (2021) — 590 citations