Learning the Parameters of Deep Convolutional Networks with Geodesics


Learning the Parameters of Deep Convolutional Networks with Geodesics – In this paper, a non-parametric method to measure the uncertainty of the neural network model can be presented. To this end, we study how to accurately predict the posterior distribution of the neural network models. We compare the posterior distribution of the neural network models with a standard Gaussian model for different applications. Our results, comparing three different nonparametric approaches, show that a neural network model may show more uncertainty than the Gaussian model for different real-world applications.

This paper proposes a novel non-linear optimization approach for segmenting brain structures. The objective of this study is the optimization of a non-linear, non-convex optimization problem that requires to determine if any part of a complex object exists in a pre-defined space and if so, which it will appear. We present a principled yet scalable algorithm called NodalOpt, which is based on the Nonlinear Logic Satisfiability of Multi-Layer Proxies and an efficient variant of Linearization. NodalOpt, unlike the previous two algorithms, is not restricted to the linearity assumption and allows for a simple yet efficient optimization algorithm for the whole problem. We compare the results with the previous two algorithms, and show their performance on many tasks and models.

Classification of non-mathematical data: SVM-ES and some (not all) SVM-ES

Stochastic Variational Inference with Batch and Weight Normalization

Learning the Parameters of Deep Convolutional Networks with Geodesics

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  • On the convergence of the gradient of the Hessian

    A Novel Approach for Designing Multi-Layer Imaging Agents for Hyperspectral Image InspectionThis paper proposes a novel non-linear optimization approach for segmenting brain structures. The objective of this study is the optimization of a non-linear, non-convex optimization problem that requires to determine if any part of a complex object exists in a pre-defined space and if so, which it will appear. We present a principled yet scalable algorithm called NodalOpt, which is based on the Nonlinear Logic Satisfiability of Multi-Layer Proxies and an efficient variant of Linearization. NodalOpt, unlike the previous two algorithms, is not restricted to the linearity assumption and allows for a simple yet efficient optimization algorithm for the whole problem. We compare the results with the previous two algorithms, and show their performance on many tasks and models.


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