Nicholas J. Bryan

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Nicholas J. Bryan trusts overall

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

  1. Diederik P. Kingma
  2. Masanori Morise

Papers from Nicholas J. Bryan's perspective

Ordered by how strongly the researchers Nicholas J. Bryan 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. ImageNet classification with deep convolutional neural networks
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
  8. Long Short-Term Memory
  9. Gradient-based learning applied to document recognition
  10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers