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學術活動

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演講公告
  • 標題:(108.10.7 書報討論) 主講者:中研院物理所 陳俊仲博士。
  • 公告日期:2019-10-02

 各位老師、同學們大家好,

本次邀請校外學者蒞臨演講,活動相關訊息如下,請參考。
演講時間:108年10月7日(一)14:10~15:30
演講場地:志希樓理學院會議室
主講人: 中研院物理所 陳俊仲博士
演講主題:
Statistical Modeling with Artificial Neural Network

摘要
Dynamical data from neural recording of brains and from simulations of spiking neuron networks can be studied with statistical models such as the pair-wise-coupled spin-glass model. The statistical properties of the model systems allow characterization of system states with thermodynamic phases and predictions of the neural dynamics using statistical mechanics. However, common approaches to find the parameters that map the measurements to the models generally rely on the iterative Boltzmann learning (BL) that can be computationally costly and scale poorly with the system size. To speed up the process for a broader application of the approach, various artificial neural networks, including convolutional neural networks (CNN) and restricted Boltzmann machines are used in conjunction with the BL. The speed up and range of applicability are assessed by applying the methods to randomly generated spin-glass systems. Treating the covariance matrix of the data as an image, a properly trained CNN works surprisingly well in several system cases considered. By using the calculation results from the iterations of BL as the training data for the CNN and using the CNN predictions as initial guess of the BL, we can alleviate the burden of finding a proper training data set beforehand for the CNN and speed up the subsequent BL processes with better initial guesses.

 

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  • 最後修改時間:2019-10-02 PM 4:11

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