Takashi Bando

2017

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

Whom Takashi Bando trusts

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

  1. Paul Smolensky (in Probabilistic & Bayesian Methods)

Papers from Takashi Bando's perspective

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

  1. Learning Deep Architectures for AI
  2. Learning representations by back-propagating errors
  3. Information processing in dynamical systems: foundations of harmony theory
  4. Independent component analysis: algorithms and applications
  5. Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
  6. Principal Component Analysis
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  9. ImageNet classification with deep convolutional neural networks
  10. Gradient-based learning applied to document recognition

Their papers