Efficient Online Convex Optimization with a Non-Convex Cost Function


Efficient Online Convex Optimization with a Non-Convex Cost Function – Convolutional neural networks (CNN) have shown that they can achieve good predictive performance for different tasks. In this paper, we propose a novel algorithm for non-convex learning in CNNs. We build the CNN to efficiently learn the global sparse structure between two images in an online fashion. Then, we compute the loss function along with the underlying non-convex cost function in the CNN. The network can be trained in any state which preserves the sparsity of the image, which makes it suitable for many tasks. Our main contributions are: 1) we exploit the sparsity in CNNs for learning the loss function in a non-convex fashion. 2) we develop a general-domain CNN to learn the loss function by building a loss function that can be learned efficiently. 3) we conduct extensive experiments to show that our CNN can dramatically outperform state-of-the-art CNN-based systems when considering the sparse representation of images of the image.

We propose a novel and effective method to automatically learn the basis of models’ beliefs from images. We first show that the assumption of beliefs is a necessary condition for learning a model from images. Second, we propose an algorithm to learn the basis of model’s belief. Lastly, we use a novel, simple, and effective feature-based approach to learn the belief structure of models. These features, together with semantic information we provide on model’s beliefs, allow us to generalize the framework to the many domains with better generalizations. Our model is trained end-to-end using a state-of-the-art neural network that we have used for training.

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Efficient Online Convex Optimization with a Non-Convex Cost Function

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  • Efficient Online Sufficient Statistics for Transfer in Machine Learning with Deep Learning

    Proceedings of the 2010 ICML Workshop on Disbelief in Artificial Intelligence (W3 2010)We propose a novel and effective method to automatically learn the basis of models’ beliefs from images. We first show that the assumption of beliefs is a necessary condition for learning a model from images. Second, we propose an algorithm to learn the basis of model’s belief. Lastly, we use a novel, simple, and effective feature-based approach to learn the belief structure of models. These features, together with semantic information we provide on model’s beliefs, allow us to generalize the framework to the many domains with better generalizations. Our model is trained end-to-end using a state-of-the-art neural network that we have used for training.


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