projects:hope:start
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| projects:hope:start [2015/02/11 01:58] – hj | projects:hope:start [2016/04/25 14:55] (current) – hj | ||
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| ====== HOPE ====== | ====== HOPE ====== | ||
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| ===== Hybrid Orthogonal Projection & Estimation (HOPE) ===== | ===== Hybrid Orthogonal Projection & Estimation (HOPE) ===== | ||
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| Each hidden layer in neural networks can be formulated as one HOPE model [1] | 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 combines a linear orthogonal projection and a mixture model under a unified generative modelling framework; |
| * The HOPE model can be used as a novel tool to probe why and how NNs work; | * 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 | + | * The HOPE model provides several new learning algorithms to learn NNs in either |
| **Reference: | **Reference: | ||
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| [1] //Shiliang Zhang and Hui Jiang//, " | [1] //Shiliang Zhang and Hui Jiang//, " | ||
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| + | [2] //Shiliang Zhang, Hui Jiang, Lirong Dai//, "The New HOPE Way to Learn Neural Networks", | ||
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| + | [3] //S. Zhang, H. Jiang, L. Dai//, “[[http:// | ||
| **Software: | **Software: | ||
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| - | The matlab codes to reproduce the MNIST results in [1] can be downloaded here. | + | The matlab codes to reproduce the MNIST results in [1,2] can be downloaded |
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