Hyuntak Cha
Artificial Intelligence · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 69 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 33 citations into it; #9,455 of 35,345 by trust here, #14,181 by citations
- Computer Vision and Pattern Recognition — 31 citations into it; #6,630 of 21,575 by trust here, #9,722 by citations
- Representation & Self-Supervised Learning — 23 citations into it; #1,148 of 3,145 by trust here, #1,781 by citations
- Meta & Continual Learning — 12 citations into it; #267 of 1,041 by trust here, #502 by citations
- Optimization & Training — 6 citations into it
Whom Hyuntak Cha trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Arslan Chaudhry
- Yann LeCun
- Geoffrey E. Hinton
- Yoshua Bengio
- Marc'Aurelio Ranzato
- Diederik P. Kingma
- L. Eon Bottou
- Ross B. Girshick
Who trusts Hyuntak Cha overall
- Gyuhak Kim
- Zifeng Wang
- Marco Toldo
- Abdulaziz S. Alakooz
- Lorenzo Bonicelli
- Yabin Wang
- Prashant Bhat
- Arjun Ashok
- P. Janson
- Matteo Boschini
Papers from Hyuntak Cha's perspective
Ordered by how strongly the researchers Hyuntak Cha 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Co2L: Contrastive Continual Learning (2021) — 408 citations
- Learning from Failure: De-biasing Classifier from Biased Classifier (2020) — 416 citations