J. Eschbacher

Computer Vision and Pattern Recognition · 2009–2018

A researcher in the Science corpus: 15 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom J. Eschbacher trusts overall

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

  1. Alex Krizhevsky
  2. Christian Szegedy
  3. Geoffrey E. Hinton
  4. Yann LeCun

Papers from J. Eschbacher's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Going deeper with convolutions
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Rethinking the Inception Architecture for Computer Vision
  5. Gradient-based learning applied to document recognition
  6. Caffe: Convolutional Architecture for Fast Feature Embedding
  7. Long Short-Term Memory
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. Attention is All you Need (Neural Information Processing Systems)
  10. Learning Multiple Layers of Features from Tiny Images

Their papers