Nabarun Deb

Statistics and Probability · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 57 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 77 citations into it; #3,669 of 12,932 by trust here, #7,165 by citations
• Artificial Intelligence — 33 citations into it
• Statistical Learning Theory — 21 citations into it
• Finance — 12 citations into it
• Applied Mathematics — 11 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Geometry and Topology — 7 citations into it
• Statistical and Nonlinear Physics — 7 citations into it; #5,834 of 10,127 by trust here, #9,324 by citations

Whom Nabarun Deb trusts overall

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

1. G'abor J. Sz'ekely
2. Arthur Gretton
3. Maria L. Rizzo
4. Kenji Fukumizu
5. Sara Taskinen
6. Hannu Oja
7. Cédric Villani
8. Michael R. Kosorok
9. W. Hoeffding
10. Sourav Chatterjee

Who trusts Nabarun Deb overall

1. Hongjian Shi
2. Mathias Drton
3. Fang Han
4. Promit Ghosal
5. Zhexiao Lin
6. Marc Hallin
7. Sourav Chatterjee
8. Marcel Nutz
9. Bodhisattva Sen
10. Espen Bernton

Papers from Nabarun Deb's perspective

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

Their papers

• Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation (2019) — 175 citations
• Measuring Association on Topological Spaces Using Kernels and Geometric Graphs (2020) — 69 citations