Amir Navot

Statistical Learning Theory · 2004–2005

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

Whom Amir Navot trusts overall

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

  1. Sanjoy Dasgupta
  2. Peter L. Bartlett
  3. Yoav Freund
  4. Sam T. Roweis
  5. Corinna Cortes
  6. Ron Kohavi
  7. Naftali Tishby
  8. Robert E. Schapire
  9. Vladimir Naumovich Vapnik
  10. Jason Aaron Edward Weston

Who trusts Amir Navot overall

  1. Elena Marchiori
  2. Charu C. Aggarwal
  3. H. Sosik
  4. Amir Globerson
  5. Samarth Sinha
  6. Philip Bachman
  7. Wei Yang
  8. Shuang Gao
  9. Athanasios Tsanas
  10. Amin Ul Haq

Papers from Amir Navot's perspective

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

  1. Nonlinear dimensionality reduction by locally linear embedding.
  2. Support-Vector Networks
  3. ImageNet classification with deep convolutional neural networks
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. Gradient-based learning applied to document recognition
  6. The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network
  7. A decision-theoretic generalization of on-line learning and an application to boosting
  8. Structural Risk Minimization Over Data-Dependent Hierarchies
  9. The information bottleneck method
  10. Boosting the margin: A new explanation for the effectiveness of voting methods

Their papers