Mo Bavarian

Artificial Intelligence · 2021–2025

A researcher in the Science corpus: 7 papers; trusts 12 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Mo Bavarian trusts overall

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

  1. Alec Radford
  2. Geoffrey E. Hinton
  3. Diederik P. Kingma
  4. Yoshua Bengio
  5. Ilya Sutskever
  6. Ashish Vaswani
  7. Alex Krizhevsky
  8. Kaiming He
  9. Yann LeCun
  10. Alex Graves

Who trusts Mo Bavarian overall

  1. Yongchao Zhou
  2. Daya Guo
  3. Erik Nijkamp
  4. Zhihong Shao
  5. Yao Fu
  6. Baptiste Rozière
  7. Daniel Fried
  8. Xuezhi Wang
  9. Jason Wei
  10. Jie Huang

Papers from Mo Bavarian's perspective

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

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

Their papers