Philip Schmidt

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Philip Schmidt trusts overall

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

  1. Sylvia D. Kreibig
  2. Attila Reiss
  3. Jonghwa Kim
  4. William S. Helton
  5. Cornelia Setz
  6. Karen Simonyan
  7. Didier Stricker
  8. Kaiming He
  9. Sergey Ioffe
  10. Sander Koelstra

Who trusts Philip Schmidt overall

  1. Pritam Sarkar
  2. Shruti Gedam
  3. Hayden S. Helm
  4. Ali Etemad
  5. Sanchita Paul
  6. Yan Wang

Papers from Philip Schmidt's perspective

Ordered by how strongly the researchers Philip Schmidt 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  4. ImageNet classification with deep convolutional neural networks
  5. Gradient-based learning applied to document recognition
  6. Long Short-Term Memory
  7. Densely Connected Convolutional Networks
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. ImageNet Large Scale Visual Recognition Challenge
  10. Attention is All you Need (Neural Information Processing Systems)

Their papers