Weijie Su
Artificial Intelligence · with Candes · 2014–2021
A researcher in the Science corpus: 11 papers; trusts 119 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 127 citations into it; #6,870 of 35,345 by trust here, #8,810 by citations
- Statistics and Probability — 110 citations into it; #2,504 of 12,932 by trust here, #2,629 by citations
- Computational Mechanics — 52 citations into it; #1,114 of 10,001 by trust here, #1,816 by citations
- Optimization & Training — 52 citations into it; #848 of 2,601 by trust here, #1,237 by citations
- Privacy & Security — 42 citations into it
- Management Science and Operations Research — 31 citations into it; #7,343 of 11,946 by trust here, #9,383 by citations
- Computational Theory and Mathematics — 27 citations into it; #7,884 of 12,182 by trust here, #5,239 by citations
- Computer Vision and Pattern Recognition — 25 citations into it
Whom Weijie Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- E.J. Candes
- Yoav Benjamini
- Robert Tibshirani
- David L. Donoho
- Alex Krizhevsky
- Cun-Hui Zhang
- Paul Tseng
- Cynthia Dwork
- Hui Zou
Who trusts Weijie Su overall
- Hédy Attouch
- Richard Nock
- Andre Wibisono
- Ashia C. Wilson
- Yura Malitsky
- Michael I. Jordan
- Mahmoud Assran
- E.J. Candes
- Bin Shi
- James Lucas
Papers from Weijie Su's perspective
Ordered by how strongly the researchers Weijie Su 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
- ImageNet: A large-scale hierarchical image database
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights (2014) — 1,385 citations
- SLOPE—ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION (2014) — 318 citations
- Christine P. Chai's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al. (2021) — 206 citations
- Private False Discovery Rate Control (2015) — 28 citations
- Familywise error rate control via knockoffs (2015) — 51 citations
- SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax (2015) — 150 citations
- On Learning Rates and Schrödinger Operators (2020) — 71 citations
- Deep Learning with Gaussian Differential Privacy (2019) — 250 citations
- Gaussian Di ↵ erential Privacy (2019)
- Assumption Lean Regression (2018) — 28 citations
- Understanding the acceleration phenomenon via high-resolution differential equations (2018) — 336 citations