Thomas A. Lasko
Artificial Intelligence · 2005–2017
A researcher in the Science corpus: 5 papers; trusts 13 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 21 citations into it; #11,921 of 35,345 by trust here, #7,858 by citations
- Statistics and Probability — 17 citations into it; #11,472 of 12,932 by trust here, #5,661 by citations
- Computer Vision and Pattern Recognition — 10 citations into it
- Representation & Self-Supervised Learning — 6 citations into it; #1,492 of 3,145 by trust here, #1,671 by citations
Whom Thomas A. Lasko trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Quoc V. Le
- Jerome H. Friedman
- Honglak Lee
- Trevor J. Hastie
- Hui Zou
- L. V. D. van der Maaten
- Andrew Y. Ng
- Dong C. Liu
Who trusts Thomas A. Lasko overall
- Zhengping Che
- D. Streiner
- Zachary Chase Lipton
- Hossein Soleimani
- Narges Razavian
- Sanjay Purushotham
- Phuoc Nguyen
- Benjamin Shickel
- Pranjul Yadav
- Marzyeh Ghassemi
Papers from Thomas A. Lasko's perspective
Ordered by how strongly the researchers Thomas A. Lasko 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
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data (2013) — 317 citations
- The use of receiver operating characteristic curves in biomedical informatics (2005) — 884 citations
- Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals (2017) — 87 citations
- Predicting Medications from Diagnostic Codes with Recurrent Neural Networks (2016) — 59 citations
- Portability of an algorithm to identify rheumatoid arthritis in electronic health records (2012) — 230 citations