Julian Bock

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Julian Bock trusts overall

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

  1. Shaoqing Ren
  2. Olaf Ronneberger
  3. Alexandre Robicquet
  4. Stefano Pellegrini
  5. Robert Krajewski
  6. Kaiming He
  7. Ross B. Girshick
  8. Fabian Poggenhans
  9. Wei Zhan
  10. Philipp Fischer

Who trusts Julian Bock overall

  1. Karttikeya Mangalam
  2. Shangding Gu

Papers from Julian Bock's perspective

Ordered by how strongly the researchers Julian Bock 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. ImageNet Large Scale Visual Recognition Challenge
  5. Attention is All you Need (Neural Information Processing Systems)
  6. Long Short-Term Memory
  7. ImageNet: A large-scale hierarchical image database
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. Learning Multiple Layers of Features from Tiny Images
  10. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Their papers