Taesup Moon

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 14 papers; trusts 1,057 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 266 citations into it; #4,385 of 35,345 by trust here, #4,747 by citations
• Computer Vision and Pattern Recognition — 191 citations into it; #4,483 of 21,575 by trust here, #4,469 by citations
• Representation & Self-Supervised Learning — 72 citations into it
• Meta & Continual Learning — 71 citations into it; #123 of 1,041 by trust here, #133 by citations
• Optimization & Training — 50 citations into it; #690 of 2,601 by trust here, #685 by citations
• Model Architectures — 36 citations into it
• Reinforcement Learning — 34 citations into it; #1,957 of 2,242 by trust here, #1,932 by citations
• Safety, Robustness & Interpretability — 34 citations into it; #1,537 of 2,251 by trust here, #1,857 by citations

Whom Taesup Moon trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Graves
7. Yann LeCun
8. Ilya Sutskever
9. Karen Simonyan
10. Ruslan Salakhutdinov

Who trusts Taesup Moon overall

1. Mirco Ravanelli
2. Yarin Gal
3. Liyuan Wang
4. Yoshua Bengio
5. Dmitriy Serdyuk
6. Lihong Li
7. Shimi Salant
8. Stanislau Semeniuta
9. Seonwoo Min
10. Jorge Armando Mendez Mendez

Papers from Taesup Moon's perspective

Ordered by how strongly the researchers Taesup Moon 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• SS-IL: Separated Softmax for Incremental Learning (2020) — 219 citations
• RNNDROP: A novel dropout for RNNS in ASR (2015) — 114 citations
• Uncertainty-based Continual Learning with Adaptive Regularization (2019) — 274 citations
• Fooling Neural Network Interpretations via Adversarial Model Manipulation (2019) — 241 citations
• Refining Recency Search Results with User Click Feedback (2011) — 27 citations
• Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks (2016) — 552 citations
• Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning (2022) — 3 citations
• SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (2021) — 135 citations
• CPR: Classifier-Projection Regularization for Continual Learning (2020) — 91 citations
• A Simple Class Decision Balancing for Incremental Learning (2020) — 7 citations
• Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (2020) — 163 citations
• Adaptive Group Sparse Regularization for Continual Learning (2020) — 8 citations
• Learning to model relatedness for news recommendation (2011) — 104 citations
• An unbiased offline evaluation of contextual bandit algorithms with generalized linear models (2011) — 82 citations