Dami Choi

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 106 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #20,551 of 35,345 by trust here, #13,163 by citations
• Optimization & Training — 67 citations into it; #1,736 of 2,601 by trust here, #1,225 by citations
• Computer Vision and Pattern Recognition — 41 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it
• Reinforcement Learning — 17 citations into it
• Generative Models — 13 citations into it
• Model Architectures — 13 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Dami Choi trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Ronald J. Williams
7. Ilya Sutskever
8. Yann LeCun
9. Jimmy Ba
10. Christian Szegedy

Who trusts Dami Choi overall

1. Arash Vahdat
2. Avishek Joey Bose
3. Yuxi Li
4. Prabhu Teja Sivaprasad
5. Ching-An Cheng
6. Evgeny Andriyash
7. Vaden Masrani
8. Krzysztof Marcin Choromanski
9. Atilim Gunes Baydin
10. Guy Lorberbom

Papers from Dami Choi's perspective

Ordered by how strongly the researchers Dami Choi 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Backpropagation through the Void: Optimizing control variates for black-box gradient estimation (2017) — 317 citations
• Gradient Estimation with Stochastic Softmax Tricks (2020) — 101 citations
• On Empirical Comparisons of Optimizers for Deep Learning (2019) — 304 citations