Pengkun Yang
Artificial Intelligence · 2014–2020
A researcher in the Science corpus: 4 papers; trusts 55 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 60 citations into it; #5,246 of 35,345 by trust here, #13,353 by citations
- Statistical Learning Theory — 22 citations into it
- Statistics and Probability — 22 citations into it; #4,946 of 12,932 by trust here, #9,233 by citations
- Statistical and Nonlinear Physics — 19 citations into it; #8,683 of 10,127 by trust here, #9,953 by citations
- Optimization & Training — 18 citations into it
- Computer Vision and Pattern Recognition — 17 citations into it
- Scaling & Learning Theory — 16 citations into it; #213 of 428 by trust here, #384 by citations
- Model Architectures — 11 citations into it
Whom Pengkun Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Liam Paninski
- Alexandre B. Tsybakov
- Paul Valiant
- Yuanzhi Li
- Gregory Valiant
- Tommaso Cai
- Lucien Le Cam
- Michael M. Mitzenmacher
- Jiantao Jiao
- Mark J van Rossum
Who trusts Pengkun Yang overall
- Jiantao Jiao
- Jayadev Acharya
- Yanjun Han
- Xiyang Liu
- Weihao Gao
- Sewoong Oh
- Yihong Wu
- Tsachy Weissman
- Kartik Venkat
- Lijie Chen
Papers from Pengkun Yang's perspective
Ordered by how strongly the researchers Pengkun Yang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
Their papers
- Chebyshev polynomials, moment matching, and optimal estimation of the unseen (2015) — 119 citations
- Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks (2020) — 63 citations
- On Learning Over-parameterized Neural Networks: A Functional Approximation Prospective (2019) — 62 citations
- Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation (2014) — 241 citations