Madeleine Udell
Artificial Intelligence · 2014–2019
A researcher in the Science corpus: 6 papers; trusts 73 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 64 citations into it; #31,077 of 35,345 by trust here, #22,463 by citations
- Computational Mechanics — 54 citations into it; #8,707 of 10,001 by trust here, #6,877 by citations
- Optimization & Training — 30 citations into it
- Safety, Robustness & Interpretability — 27 citations into it
- Computer Vision and Pattern Recognition — 26 citations into it
- Signal Processing — 20 citations into it
- Computational Theory and Mathematics — 15 citations into it
- Numerical Analysis — 15 citations into it; #3,194 of 3,586 by trust here, #3,057 by citations
Whom Madeleine Udell trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- Nati Srebro
- Trevor J. Hastie
- B. Recht
- Geoffrey E. Hinton
- Tomas Mikolov
- Daniel D. Lee
- Hyunsoo Kim
- Robert Tibshirani
- M. Soltanolkotabi
Who trusts Madeleine Udell overall
Papers from Madeleine Udell's perspective
Ordered by how strongly the researchers Madeleine Udell trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- "Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations (2019) — 124 citations
- SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
- MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
- Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved (2018) — 270 citations
- Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage (2017) — 100 citations
- Generalized Low Rank Models (2014) — 386 citations