Sun-Yuan Kung

Artificial Intelligence · 1996–2014

A researcher in the Science corpus: 5 papers; trusts 99 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Computer Networks and Communications — 27 citations into it; #2,045 of 9,886 by trust here, #2,300 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #12,028 of 21,575 by trust here, #20,756 by citations
• Electrical and Electronic Engineering — 23 citations into it; #2,255 of 16,539 by trust here, #6,381 by citations
• Molecular Biology — 22 citations into it
• Statistics and Probability — 20 citations into it; #7,018 of 12,932 by trust here, #11,487 by citations
• Statistical Learning Theory — 15 citations into it
• Computational Theory and Mathematics — 9 citations into it

Whom Sun-Yuan Kung trusts overall

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

1. Bradley Efron
2. Vladimir Naumovich Vapnik
3. Bernhard Schölkopf
4. Robert Tibshirani
5. Vladimir Naumovich Vapni
6. Christopher K. I. Williams
7. Teuvo Kohonen
8. Grigorios Tsoumakas
9. Jason Aaron Edward Weston
10. John C. Platt

Who trusts Sun-Yuan Kung overall

1. Cengiz Pehlevan
2. Tao Hu
3. Petar Popovski
4. Foad Sohrabi
5. Ahmed Alkhateeb
6. Fernando De la Torre
7. Bernhard Schölkopf
8. Dmitri "Mitya" Chklovskii
9. Sachin Rajsekhar Katti
10. Gabriel Cadamuro

Papers from Sun-Yuan Kung's perspective

Ordered by how strongly the researchers Sun-Yuan Kung 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. 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

• Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection (2005) — 626 citations
• Minimum-energy multicast in mobile ad hoc networks using network coding (2004) — 434 citations
• Kernel Methods and Machine Learning (2014) — 239 citations
• Information Exchange in Wireless Networks with Network Coding and Physical-layer Broadcast (2004) — 804 citations
• Principal Component Neural Networks: Theory and Applications (1996)