Lester W. Mackey
Artificial Intelligence · 2010–2021
A researcher in the Science corpus: 14 papers; trusts 482 researchers and is trusted by 59 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 176 citations into it; #4,179 of 35,345 by trust here, #9,369 by citations
- Computer Vision and Pattern Recognition — 120 citations into it; #14,617 of 21,575 by trust here, #12,172 by citations
- Statistics and Probability — 102 citations into it; #1,252 of 12,932 by trust here, #1,751 by citations
- Optimization & Training — 46 citations into it
- Computational Mechanics — 41 citations into it; #4,191 of 10,001 by trust here, #5,813 by citations
- Efficiency & Compression — 38 citations into it
- Model Architectures — 34 citations into it
- Statistical and Nonlinear Physics — 27 citations into it; #7,185 of 10,127 by trust here, #6,421 by citations
Whom Lester W. Mackey trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Radford M. Neal
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Diederik P. Kingma
- Max Welling
- Ilya Sutskever
- Karen Simonyan
Who trusts Lester W. Mackey overall
- Qiang Liu
- Michael W. Mahoney
- Yang Song
- Christopher Nemeth
- Jiaming Song
- Ramon van Handel
- Sourav Chatterjee
- Chris J. Oates
- Alex Gittens
- Jiashi Feng
Papers from Lester W. Mackey's perspective
Ordered by how strongly the researchers Lester W. Mackey 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- 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
- Measuring Sample Quality with Stein's Method (2015) — 252 citations
- Measuring Sample Quality with Kernels (2017) — 263 citations
- Jet-images — deep learning edition (2015) — 344 citations
- Matrix concentration inequalities via the method of exchangeable pairs (2012) — 177 citations
- Measuring Sample Quality with Diffusions (2016) — 123 citations
- Accurate Inference for Adaptive Linear Models (2017) — 72 citations
- Initialization and Regularization of Factorized Neural Layers (2021) — 78 citations
- Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms (2020) — 345 citations
- Teacher-Student Compression with Generative Adversarial Networks (2018) — 19 citations
- Model Compression with Generative Adversarial Networks (2018) — 10 citations
- DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation (2018) — 25 citations
- Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014) — 208 citations
- Divide-and-Conquer Matrix Factorization (2011) — 204 citations
- On the Consistency of Ranking Algorithms (2010) — 106 citations