Rishabh Agarwal

Artificial Intelligence · with Wang · 2023–2025

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Rishabh Agarwal trusts overall

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

  1. Geoffrey E. Hinton
  2. Chin-Yew Lin
  3. Oriol Vinyals
  4. Yoon Kim
  5. Yoshua Bengio
  6. Diederik P. Kingma
  7. Ilya Sutskever
  8. Alex Krizhevsky
  9. Yann LeCun
  10. Alex Graves

Papers from Rishabh Agarwal's perspective

Ordered by how strongly the researchers Rishabh Agarwal 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. Attention is All you Need (Neural Information Processing Systems)
  3. ImageNet classification with deep convolutional neural networks
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  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. Gradient-based learning applied to document recognition
  8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  9. ImageNet: A large-scale hierarchical image database
  10. Distilling the Knowledge in a Neural Network (arXiv.org)

Their papers