Sumeetpal S. Singh

Artificial Intelligence · 2003–2018

A researcher in the Science corpus: 7 papers; trusts 28 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #8,092 of 35,345 by trust here, #10,798 by citations
• Probabilistic & Bayesian Methods — 37 citations into it
• Statistics and Probability — 28 citations into it; #11,407 of 12,932 by trust here, #6,552 by citations
• Control and Systems Engineering — 12 citations into it; #7,647 of 8,257 by trust here, #3,682 by citations
• Reinforcement Learning — 9 citations into it
• Optimization & Training — 8 citations into it
• Computer Networks and Communications — 7 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Sumeetpal S. Singh trusts overall

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

1. Arnaud Doucet
2. Walter R. Gilks
3. Pierre Del Moral
4. Daryl J. Daley
5. Geir Storvik
6. Simon J. Godsill
7. Neil J. Gordon
8. Nicolas Chopin
9. Mark Briers
10. Ronald J. Williams

Who trusts Sumeetpal S. Singh overall

1. B. Vo
2. Johannes Koch
3. Olivier Capp'e
4. Dinh Q. Phung
5. B. Ristic
6. Ba-Ngu Vo
7. Laurent E. Calvet
8. Thomas Bo Schön
9. Bahador Khaleghi
10. John Mullane

Papers from Sumeetpal S. Singh's perspective

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

1. Long Short-Term Memory
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. ImageNet classification with deep convolutional neural networks
4. Proximal Policy Optimization Algorithms (arXiv.org)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Sequential Monte Carlo Methods in Practice
9. Following a moving target—Monte Carlo inference for dynamic Bayesian models
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• On Particle Methods for Parameter Estimation in State-Space Models (2014) — 478 citations
• An overview of sequential Monte Carlo methods for parameter estimation in general state-space models (2009) — 312 citations
• Filtering via approximate Bayesian computation (2010) — 103 citations
• Model-Based Reinforcement Learning for Sepsis Treatment (2018) — 60 citations
• Sequential Monte Carlo methods for multitarget filtering with random finite sets (2005) — 1,280 citations
• Particle methods for change detection, system identification, and control (2004) — 346 citations
• Sequential monte carlo implementation of the phd filter for multi-target tracking (2003) — 337 citations