Taesup Kim

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 9 papers; trusts 509 researchers and is trusted by 305 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 193 citations into it; #8,566 of 35,345 by trust here, #3,192 by citations
• Computer Vision and Pattern Recognition — 117 citations into it; #2,279 of 21,575 by trust here, #5,161 by citations
• Natural Language Processing — 54 citations into it
• Representation & Self-Supervised Learning — 40 citations into it
• Optimization & Training — 34 citations into it
• Reinforcement Learning — 34 citations into it
• Model Architectures — 33 citations into it
• Evaluation & Benchmarks — 23 citations into it

Whom Taesup Kim trusts overall

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

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

Who trusts Taesup Kim overall

1. Yilun Du
2. Igor Mordatch
3. Erik Nijkamp
4. Ruiqi Gao
5. Yoshua Bengio
6. Song-Chun Zhu
7. Chelsea Finn
8. Sergey Levine
9. Ying Nian Wu
10. Tian Han

Papers from Taesup Kim's perspective

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

Their papers

• Fast AutoAugment (2019) — 659 citations
• PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples (2017) — 846 citations
• Deep Directed Generative Models with Energy-Based Probability Estimation (2016) — 141 citations
• Bayesian Model-Agnostic Meta-Learning (2018) — 548 citations
• A Deep Reinforcement Learning Chatbot (2017) — 245 citations
• Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition (2017) — 71 citations
• Scalable Neural Architecture Search for 3D Medical Image Segmentation (2019) — 84 citations
• Edge-Labeling Graph Neural Network for Few-Shot Learning (2019) — 529 citations
• The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017)