Aashish Agarwal

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Aashish Agarwal trusts overall

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

  1. Alex Graves
  2. Awni Y. Hannun
  3. Satoshi Imai
  4. Martín Abadi
  5. Santiago Fernández
  6. J. Urgen Schmidhuber
  7. Diederik P. Kingma
  8. Faustino J. Gomez
  9. Geoffrey E. Hinton
  10. Hank Liao

Who trusts Aashish Agarwal overall

  1. Pascal Puchtler
  2. Changhan Wang
  3. Johannes Wirth
  4. René Peinl
  5. Morgane Rivière

Papers from Aashish Agarwal's perspective

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

  1. Long Short-Term Memory
  2. ImageNet classification with deep convolutional neural networks
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Gradient-based learning applied to document recognition
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  8. Learning Multiple Layers of Features from Tiny Images
  9. Going deeper with convolutions
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers