Jacob Austin

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 9 papers; trusts 99 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom Jacob Austin trusts overall

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

  1. Diederik P. Kingma
  2. Geoffrey E. Hinton
  3. Alex Krizhevsky
  4. Yoshua Bengio
  5. Ilya Sutskever
  6. Alex Graves
  7. Yann LeCun
  8. Tim Salimans
  9. Danilo Jimenez Rezende
  10. Kaiming He

Who trusts Jacob Austin overall

  1. Eliya Nachmani
  2. Chun Xia
  3. Freda Shi
  4. Angelica Chen
  5. Xinyun Chen
  6. Zhiqing Sun
  7. Huiwen Chang
  8. Ting-Li Chen
  9. Namrata Anand
  10. Federico Cassano

Papers from Jacob Austin's perspective

Ordered by how strongly the researchers Jacob Austin 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. Attention is All you Need (Neural Information Processing Systems)
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. Long Short-Term Memory
  6. Gradient-based learning applied to document recognition
  7. ImageNet: A large-scale hierarchical image database
  8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  10. Learning Multiple Layers of Features from Tiny Images

Their papers