Sanjay Purushotham

Artificial Intelligence · 2012–2018

A researcher in the Science corpus: 6 papers; trusts 83 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #27,362 of 35,345 by trust here, #7,346 by citations
• Model Architectures — 38 citations into it; #1,779 of 2,278 by trust here, #1,048 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Signal Processing — 30 citations into it; #5,798 of 7,572 by trust here, #918 by citations
• Natural Language Processing — 22 citations into it
• Molecular Biology — 18 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Medical & Health AI — 15 citations into it; #60 of 239 by trust here, #27 by citations

Whom Sanjay Purushotham trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Jerome H. Friedman
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ruslan Salakhutdinov
7. Ilya Sutskever
8. David M. Blei
9. Yann LeCun
10. Sepp Hochreiter

Who trusts Sanjay Purushotham overall

1. Hao Wang
2. Dit-Yan Yeung
3. Allison June-Barlow Chaney
4. Alfredo Vellido
5. Joseph D. Futoma
6. Umang Bhatt
7. Bret A. Nestor
8. Hrayr Harutyunyan
9. Yinchong Yang
10. Sarah Tan

Papers from Sanjay Purushotham's perspective

Ordered by how strongly the researchers Sanjay Purushotham trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Recurrent Neural Networks for Multivariate Time Series with Missing Values (2016) — 2,465 citations
• Benchmarking deep learning models on large healthcare datasets (2018) — 399 citations
• Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets (2017) — 85 citations
• Interpretable Deep Models for ICU Outcome Prediction (2016) — 293 citations
• Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015) — 147 citations
• Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (2012) — 195 citations