Mohammad Taha Bahadori

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 9 papers; trusts 147 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it; #8,922 of 35,345 by trust here, #5,153 by citations
• Computer Vision and Pattern Recognition — 55 citations into it
• Representation & Self-Supervised Learning — 53 citations into it
• Natural Language Processing — 36 citations into it
• Model Architectures — 28 citations into it; #1,370 of 2,278 by trust here, #950 by citations
• Optimization & Training — 24 citations into it; #2,594 of 2,601 by trust here, #2,259 by citations
• Signal Processing — 23 citations into it; #4,087 of 7,572 by trust here, #2,276 by citations
• Causal Inference & Discovery — 20 citations into it; #361 of 364 by trust here, #327 by citations

Whom Mohammad Taha Bahadori trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Graves
4. Ilya Sutskever
5. Alex Krizhevsky
6. Tomas Mikolov
7. Yann LeCun
8. Diederik P. Kingma
9. Sepp Hochreiter
10. J. Urgen Schmidhuber

Who trusts Mohammad Taha Bahadori overall

1. Edward Choi
2. Cao Xiao
3. Wei Zhang
4. Bryan Lim
5. Yuan Ling
6. Bernhard Schölkopf
7. Junyuan Shang
8. Jimeng Sun
9. Fei Wang
10. Fenglong Ma

Papers from Mohammad Taha Bahadori's perspective

Ordered by how strongly the researchers Mohammad Taha Bahadori 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism (2016) — 1,544 citations
• GRAM: Graph-based Attention Model for Healthcare Representation Learning (2016) — 770 citations
• Deep Computational Phenotyping (2015) — 267 citations
• Multi-layer Representation Learning for Medical Concepts (2016) — 540 citations
• Causal Regularization (2019) — 57 citations
• Causal Regularization (2017)
• FLASH: Fast Bayesian Optimization for Data Analytic Pipelines (2016) — 35 citations
• Causal Phenotype Discovery via Deep Networks (2015) — 36 citations
• Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015)