Minsuk Kahng

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 5 papers; trusts 143 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #16,637 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 94 citations into it; #7,822 of 21,575 by trust here, #8,952 by citations
• Safety, Robustness & Interpretability — 46 citations into it
• Natural Language Processing — 21 citations into it
• Biophysics — 14 citations into it; #982 of 1,196 by trust here, #754 by citations
• Human-Computer Interaction — 11 citations into it
• Signal Processing — 11 citations into it
• Information Systems — 10 citations into it

Whom Minsuk Kahng trusts overall

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

1. Josua Krause
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Saleema Amershi
6. Todd Kulesza
7. Ilya Sutskever
8. Adam Perer
9. Adam W. Harley
10. Yoon Kim

Who trusts Minsuk Kahng overall

1. Fred Hohman
2. Sina Mohseni
3. Giulia Vilone
4. Andrew Head
5. D. V. Carvalho
6. Paolo Tamagnini
7. Neoklis Polyzotis
8. Joel Vaughan
9. Niloofar Zarei
10. Sebastian Gehrmann

Papers from Minsuk Kahng's perspective

Ordered by how strongly the researchers Minsuk Kahng trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers (2018) — 606 citations
• ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models (2017) — 351 citations
• Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
• CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization (2020) — 318 citations
• Visual exploration of machine learning results using data cube analysis (2016) — 82 citations