Segmentation from High Dimensional Data using Gaussian Process Network Lasso


Segmentation from High Dimensional Data using Gaussian Process Network Lasso – The recent release of Convolutional Neural Networks (CNN) with deep architectures can be easily implemented, but is computationally expensive to train. Recent work has shown that the amount of data needed for training CNNs can be increased with the number of parameters used by hand. In this paper, we propose to address this problem by optimizing the CNNs’ parameters, but, in this case, they will not have access to the dictionary representation of the input data. We then propose a new algorithm, called SDS-CNN, which is able to optimize the parameters in a single run of training. Our algorithm requires only the dimension of the dataset, but reduces the training data by $O(sqrt(D))$ steps. The complexity of our algorithm is reduced to $O(sqrt{D})$ steps on average on average over each iteration. In our experiments, our algorithm runs almost twice faster than the baseline CNN, which is compared to $O(sqrt{D})$ steps. Our method can effectively be used, among its competitors, for various machine learning applications.

We develop a new method for predicting the performance of a deep neural network (DNN) trained on image classification over supervised learning. We first show that the prediction of the performance of a DNN using our method is indeed a good match for the problem. Then, we demonstrate the strength of our method, by testing on several commonly-used models including Deep CNN and ConvNets. Our results show that the proposed algorithm is very strong — we can predict the performance of over 70 CNN models.

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Segmentation from High Dimensional Data using Gaussian Process Network Lasso

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    Multivariate Student’s Test for Interventional ErrorWe develop a new method for predicting the performance of a deep neural network (DNN) trained on image classification over supervised learning. We first show that the prediction of the performance of a DNN using our method is indeed a good match for the problem. Then, we demonstrate the strength of our method, by testing on several commonly-used models including Deep CNN and ConvNets. Our results show that the proposed algorithm is very strong — we can predict the performance of over 70 CNN models.


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