T. Arakaki

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom T. Arakaki trusts overall

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

  1. Vijay Badrinarayanan
  2. Pascal Vincent
  3. Radford M. Neal

Papers from T. Arakaki's perspective

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

  1. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
  2. Pattern Recognition and Machine Learning
  3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
  4. ImageNet classification with deep convolutional neural networks
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. Gradient-based learning applied to document recognition
  7. ImageNet Large Scale Visual Recognition Challenge
  8. Going deeper with convolutions
  9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  10. Attention is All you Need (Neural Information Processing Systems)

Their papers