Yue M. Lu
Computational Mechanics · 2008–2021
A researcher in the Science corpus: 4 papers; trusts 95 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 78 citations into it; #2,832 of 10,001 by trust here, #3,908 by citations
- Artificial Intelligence — 49 citations into it; #28,118 of 35,345 by trust here, #34,213 by citations
- Computer Vision and Pattern Recognition — 31 citations into it; #8,651 of 21,575 by trust here, #16,367 by citations
- Optimization & Training — 30 citations into it
- Signal Processing — 19 citations into it; #6,005 of 7,572 by trust here, #7,486 by citations
- Statistics and Probability — 19 citations into it
- Mathematical Optimization — 16 citations into it
- Representation & Self-Supervised Learning — 16 citations into it
Whom Yue M. Lu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- Yurii E. Nesterov
- Gitta Kutyniok
- Bin Yang
- Prateek Jain
- Rong Ge
- Raghunandan H. Keshavan
- Roman Vershynin
- B. Recht
- Martin Vetterli
Who trusts Yue M. Lu overall
- Yonina C. Eldar
- Moshe Mishali
- Thomas Blumensath
- Junzhou Huang
- M. Davies
- Tong Zhang
- Marco F. Duarte
- D. P. Palomar
- Dimitris N. Metaxas
- Ronen Tur
Papers from Yue M. Lu's perspective
Ordered by how strongly the researchers Yue M. Lu 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
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- 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 Theory for Sampling Signals From a Union of Subspaces (2008) — 309 citations
- Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview (2018) — 491 citations
- Phase Transitions in Transfer Learning for High-Dimensional Perceptrons (2021) — 25 citations
- Streaming PCA and Subspace Tracking: The Missing Data Case (2018) — 101 citations