Tag Archives: Machine Learning

Tensor Methods in Machine Learning

From http://www.offconvex.org/2015/12/17/tensor-decompositions/

 

Tensors are high dimensional generalizations of matrices. In recent years tensor decompositions were used to design learning algorithms for estimating parameters of latent variable models like Hidden Markov Model, Mixture of Gaussians and Latent Dirichlet Allocation (many of these works were considered as examples of “spectral learning”, read on to find out why). In this post I will briefly describe why tensors are useful in these settings. Continue reading

Deep Learning + NLP (PFIの勉強会)

  1. Deep Learning とは?
    • Neural Network (input, hidden, output) の拡張: hidden layers >= 2(階層を増やす)
    • Hidden layers:
      • Deep Brief Network
      • Convolutional Neural Network (image recognition)
      • Stacked Auto Encoder
      • Recursive NN
      • Recurrence NN
    • 画像認識精度アップ、音声認識、薬物の活性の推測
      Continue reading
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