Neal Patwari

Electrical and Electronic Engineering · 2003–2011

A researcher in the Science corpus: 9 papers; trusts 37 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

Whom Neal Patwari trusts overall

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

  1. Joey Wilson
  2. Piyush Agrawal
  3. Robert Tibshirani
  4. G. Durgin
  5. Yi Shang
  6. Andreas Savvides
  7. Paramvir Victor Bahl
  8. Moustafa Youssef
  9. Joel A. Tropp
  10. Sebastian Thrun

Who trusts Neal Patwari overall

  1. Guoqiang Mao
  2. Joey Wilson
  3. Baris Fidan
  4. Sinan Gezici
  5. Zheng Yang
  6. Chenshu Wu
  7. Yuan Shen
  8. Moe Z. Win
  9. Henk Wymeersch
  10. Jian Liu

Papers from Neal Patwari's perspective

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

  1. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
  2. Regression Shrinkage and Selection via the Lasso
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. ImageNet classification with deep convolutional neural networks
  5. RADAR: an in-building RF-based user location and tracking system (Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064))
  6. GPS-less low-cost outdoor localization for very small devices (IEEE wireless communications)
  7. Convex position estimation in wireless sensor networks (Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213))
  8. Dynamic fine-grained localization in Ad-Hoc networks of sensors (ACM/IEEE International Conference on Mobile Computing and Networking)
  9. ImageNet: A large-scale hierarchical image database
  10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers