Munish Kumar

Computer Vision and Pattern Recognition · Archives of Computational Methods in Engineering · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Munish Kumar trusts overall

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

  1. David G. Lowe
  2. Tianqi Chen
  3. Samir S. Yadav
  4. Geoffrey E. Hinton
  5. Dan Claudiu Ciresan
  6. Abdel-rahman Mohamed
  7. Herbert Bay
  8. Matthew A. Turk
  9. Ethan Rublee
  10. Sue Han Lee

Papers from Munish Kumar's perspective

Ordered by how strongly the researchers Munish Kumar 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. Gradient-based learning applied to document recognition
  4. Distinctive Image Features from Scale-Invariant Keypoints
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. ImageNet: A large-scale hierarchical image database
  7. ImageNet Large Scale Visual Recognition Challenge
  8. Long Short-Term Memory
  9. Learning Multiple Layers of Features from Tiny Images
  10. Going deeper with convolutions

Their papers