Optimal Bounds for Online Convex Optimization Problems via Random Projections


Optimal Bounds for Online Convex Optimization Problems via Random Projections – We present a new formulation of optimal bounds, which captures the exactness of optimization in a non-convex setting — a generalization of standard optimal bounds for the optimization of bounded vectors. As we show, our formulation generalizes existing optimization framework and is much easier to apply. Our results can be used to develop new algorithms and to provide additional insight into the current state of the art.

In this paper, we propose the first generalization of the optimal bounds for the Bayesian optimization of discrete vectors based on Gaussian priors (HOG), whose complexity is a function of the number of submodular functions with Gaussian distributions. The main contribution of our work is a new formulation of optimal bounds for this problem, which captures the exactness of optimization in a non-convex setting — a generalization of standard optimal bounds for the optimization of bounded points.

We propose a new method of image-level segmentation of small-scale objects by the use of convolutional neural networks (CNN) during optimization. The CNN computes a temporal model in each convolutional layer of a CNN based on the temporal information contained in the input object. For this task, the CNN is fitted into a local memory space called a memory pool. The CNN takes care of the occlusion of the input image, which is necessary for image-level segmentation. The CNN also takes care of the segmentation of missing regions in the image layer to reduce the number of outliers. In this paper, we propose a deep neural network model named the Image-Level Subspace Model (LFSM) for segmentation of small-scale objects in image-level image. Furthermore, we show that LFSM achieves better segmentation accuracies than existing state-of-the-art CNN architectures.

A Generalized Online Convex Optimization Framework for Stochastic Nonparametric Learning

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Optimal Bounds for Online Convex Optimization Problems via Random Projections

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  • A deep residual network for event prediction

    On the Consistency of Spatial-Temporal Features for Image RecognitionWe propose a new method of image-level segmentation of small-scale objects by the use of convolutional neural networks (CNN) during optimization. The CNN computes a temporal model in each convolutional layer of a CNN based on the temporal information contained in the input object. For this task, the CNN is fitted into a local memory space called a memory pool. The CNN takes care of the occlusion of the input image, which is necessary for image-level segmentation. The CNN also takes care of the segmentation of missing regions in the image layer to reduce the number of outliers. In this paper, we propose a deep neural network model named the Image-Level Subspace Model (LFSM) for segmentation of small-scale objects in image-level image. Furthermore, we show that LFSM achieves better segmentation accuracies than existing state-of-the-art CNN architectures.


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