Rajath Kumar

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Rajath Kumar trusts overall

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

  1. Hervé Bourlard
  2. Yoshua Bengio
  3. Balakrishnan Varadarajan
  4. Thomas Schatz
  5. Douglas A. Reynolds
  6. Quoc V. Le
  7. Maarten Versteegh
  8. Daniel Renshaw

Papers from Rajath Kumar's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Long Short-Term Memory
  4. Gradient-based learning applied to document recognition
  5. Learning Deep Architectures for AI
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Attention is All you Need (Neural Information Processing Systems)
  8. The Kaldi Speech Recognition Toolkit
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. ImageNet: A large-scale hierarchical image database

Their papers