Machine Learning Lab

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====== Current Projects ======


===== Hybrid Orthogonal Projection & Estimation (HOPE) =====

{{:projects:current:one-nn-layer.png?0x220 |NN hidden layer}}{{ :projects:current:one-hope-layer.png?0x220| HOPE}}
Each hidden layer in neural networks can be formulated as one HOPE model[1]
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  * The HOPE model combines a linear orthogonal projection and a mixture model under a unied generative modelling framework; 
  * The HOPE model can be used as a novel tool to probe why and how NNs work; 
  * The HOPE model provides several new learning algorithms to learn NNs either supervisedly or unsupervisedly. 
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**Reference:** 
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[1] //Shiliang Zhang and Hui Jiang//, "Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks," [[http://arxiv.org/abs/1502.00702|arXiv:1502.00702]]. 

**Software:**
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The matlab codes to reproduce the MNIST results in [1] can be downloaded here.