Markus Schedl

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Markus Schedl trusts overall

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

  1. Fabio Aiolli
  2. Feng Niu
  3. Cai-Nicolas Ziegler
  4. Bart P. Knijnenburg
  5. James J. Gross
  6. Gediminas Adomavicius
  7. Maya Tamir
  8. Sander L. Koole
  9. S. O’Neill
  10. Anind K. Dey

Papers from Markus Schedl's perspective

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

  1. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  3. ImageNet classification with deep convolutional neural networks
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. Long Short-Term Memory
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Gradient-based learning applied to document recognition
  9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
  10. ImageNet: A large-scale hierarchical image database

Their papers