Sanjeev R. Kulkarni

Artificial Intelligence · with Poor · 1995–2020

A researcher in the Science corpus: 9 papers; trusts 55 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #11,054 of 35,345 by trust here, #11,327 by citations
• Computer Networks and Communications — 29 citations into it; #5,305 of 9,886 by trust here, #2,478 by citations
• Optimization & Training — 27 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it; #5,343 of 21,575 by trust here, #8,831 by citations
• Statistics and Probability — 25 citations into it; #11,652 of 12,932 by trust here, #5,481 by citations
• Electrical and Electronic Engineering — 23 citations into it; #9,434 of 16,539 by trust here, #14,827 by citations
• Statistical Learning Theory — 22 citations into it
• Federated & Distributed Learning — 21 citations into it

Whom Sanjeev R. Kulkarni trusts overall

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

1. Paul Thagard
2. Vladimir Naumovich Vapnik
3. Huaiyu Zhu
4. David Ruppert
5. Yuhai Wu
6. Luc Devroye
7. Michael G. Rabbat
8. Alex Krizhevsky
9. Thomas M. Cover
10. Leo Breiman

Who trusts Sanjeev R. Kulkarni overall

1. Barnab'as P'oczos
2. Samory Kpotufe
3. Lei Ying
4. Jeff Schneider
5. Fernando Perez-Cruz
6. Bharath K. Sriperumbudur
7. Gérard Biau
8. Liang Xiong
9. Danica J. Sutherland
10. Weihao Gao

Papers from Sanjeev R. Kulkarni's perspective

Ordered by how strongly the researchers Sanjeev R. Kulkarni 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances (2009) — 414 citations
• Divergence estimation of continuous distributions based on data-dependent partitions (2005) — 208 citations
• Universal Estimation of Information Measures for Analog Sources (2009) — 57 citations
• A deterministic approach to throughput scaling in wireless networks (2002) — 330 citations
• Distributed learning in wireless sensor networks (2005) — 376 citations
• Federated Learning With Quantized Global Model Updates (2020) — 160 citations
• Machine Learning Methods for Attack Detection in the Smart Grid (2015) — 550 citations
• The Problem of Induction (2006)
• Rates of convergence of nearest neighbor estimation under arbitrary sampling (1995)