Bernhard Schäfl

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Bernhard Schäfl trusts overall

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

  1. Jerome H. Friedman
  2. Sepp Hochreiter
  3. Geoffrey E. Hinton
  4. J. Urgen Schmidhuber
  5. Alex Graves
  6. Diederik P. Kingma
  7. Günter Klambauer
  8. Yoshua Bengio
  9. Yann LeCun
  10. Alex Krizhevsky

Who trusts Bernhard Schäfl overall

  1. Duncan C. McElfresh
  2. Fabian Paischer

Papers from Bernhard Schäfl's perspective

Ordered by how strongly the researchers Bernhard Schäfl 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. ImageNet classification with deep convolutional neural networks
  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. ImageNet: A large-scale hierarchical image database
  8. Learning Multiple Layers of Features from Tiny Images
  9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers