Kurt Debattista

Computer Vision and Pattern Recognition · 2011–2018

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Kurt Debattista trusts overall

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

  1. J. Urgen Schmidhuber
  2. Yoshua Bengio
  3. Diederik P. Kingma
  4. Geoffrey E. Hinton
  5. Sepp Hochreiter
  6. David E. Rumelhart
  7. Alex Krizhevsky
  8. Yann LeCun

Papers from Kurt Debattista's perspective

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

  1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  2. Long Short-Term Memory
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. ImageNet classification with deep convolutional neural networks
  5. Gradient-based learning applied to document recognition
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Learning Multiple Layers of Features from Tiny Images
  8. ImageNet: A large-scale hierarchical image database
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers