Gautam Dasarathy

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 31 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #14,375 of 35,345 by trust here, #20,377 by citations
• Management Science and Operations Research — 34 citations into it; #2,578 of 11,946 by trust here, #4,118 by citations
• Computational Theory and Mathematics — 21 citations into it; #4,473 of 12,182 by trust here, #4,850 by citations
• Optimization & Training — 21 citations into it; #1,791 of 2,601 by trust here, #1,986 by citations
• Reinforcement Learning — 15 citations into it
• Statistics and Probability — 15 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Gautam Dasarathy trusts overall

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

1. Kirthevasan Kandasamy
2. Donald R. Jones
3. Peter Auer
4. Jonas Mockus
5. Jasper Snoek
6. Kevin Swersky
7. Jeff Schneider
8. Mark Cutler
9. Barnab'as P'oczos
10. Geoffrey E. Hinton

Who trusts Gautam Dasarathy overall

1. Kirthevasan Kandasamy
2. Rajat Sen
3. Jeff Schneider
4. Barnab'as P'oczos
5. Stefan Falkner
6. Lisha Li
7. Liam Li
8. Yi-Qi Hu
9. Willie Neiswanger
10. Sanjay Shakkottai

Papers from Gautam Dasarathy's perspective

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

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

Their papers

• Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017) — 268 citations
• Multi-fidelity Gaussian Process Bandit Optimisation (2016) — 87 citations
• The Multi-fidelity Multi-armed Bandit (2016) — 44 citations
• Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations (2016) — 164 citations