Michael A. Gelbart

Artificial Intelligence · 2014–2015

A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it; #7,862 of 35,345 by trust here, #11,327 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #8,994 of 21,575 by trust here, #15,955 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Management Science and Operations Research — 11 citations into it; #4,298 of 11,946 by trust here, #3,705 by citations
• Optimization & Training — 11 citations into it
• Computational Theory and Mathematics — 9 citations into it; #11,201 of 12,182 by trust here, #6,463 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Mathematical Optimization — 6 citations into it

Whom Michael A. Gelbart trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Ruslan Salakhutdinov
4. Jasper Snoek
5. Yoshua Bengio
6. Eric Brochu
7. Alex Krizhevsky
8. Iain Murray
9. Philipp Hennig
10. Yann LeCun

Who trusts Michael A. Gelbart overall

1. Kevin Swersky
2. Benjamin Letham
3. Jasper Snoek
4. Dimitrios Stamoulis
5. Brian Karrer
6. José Miguel Hernández-Lobato
7. Donald R. Jones
8. Peter I. Frazier
9. Marco Cote
10. Maximilian Balandat

Papers from Michael A. Gelbart's perspective

Ordered by how strongly the researchers Michael A. Gelbart 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Learning Ordered Representations with Nested Dropout (2014) — 129 citations
• Bayesian Optimization with Unknown Constraints (2014) — 533 citations
• Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (2015) — 172 citations