Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.Patrick MSheridanFuxiCaiChaoDuWenMaZhengyaZhangNature nanotechnologyP. M. Sheridan, F. Cai, C. Du, W. Ma, Z. Zhang, W. D. Lu, Sparse coding with memristor networks, Nature ...
memristor, but PCM can encode the increased probability with an increase in weight value using the drift effect. Then, according to the prior (i.e., the significance of positive weights), the posterior is determined by increasing the weights inspired from the gain coding (right column)48,49....
For example, a sparse autoencoder (SAE) based DNN approach has been presented to diagnose induction motor fault, where the strategies of denoising coding, partial corruption and dropout are involved to strengthen the robustness of feature representation and prevent the overfitting of neural network [...
Sparse coding with memristor networks. Nat Nanotech, 2017, 12: 784-789 Downloaded to IP: 123.125.67.228 On: 2018-06-11 18:27:42 http://engine.scichina.com/doi/10.1007/s11432-017-9424-ySheridan, P. M.; Cai, F. X.; Du, C.; Ma, W.; Zhang, Z. Y.; Lu, W. D. Sparse ...
Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biolog...