Alexander Neitz

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Alexander Neitz trusts overall

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

  1. Diederik P. Kingma
  2. Geoffrey E. Hinton
  3. Bernhard Schölkopf
  4. Alex Krizhevsky
  5. L. Eon Bottou
  6. John Schulman
  7. Yoshua Bengio
  8. Jan Peters
  9. Yann LeCun
  10. Howard Barnum

Papers from Alexander Neitz's perspective

Ordered by how strongly the researchers Alexander Neitz 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Attention is All you Need (Neural Information Processing Systems)
  5. Long Short-Term Memory
  6. Gradient-based learning applied to document recognition
  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. GENERATIVE ADVERSARIAL NETS

Their papers