Towards Large-Margin Cost-Sensitive Deep Learning


Towards Large-Margin Cost-Sensitive Deep Learning – We demonstrate how a family of Deep Reinforcement Learning (DRL) models (FRLMs) can be applied to the Bayesian network classification problem in which a supervised learning agent must solve non-linear optimization problems over a range of unknown inputs. FRLMs model inputs with a probabilistic distribution over the underlying state spaces. In our experiments we show that FRLM models can successfully solve the Bayesian network classification problem over all inputs, and outperform the RDLM model (1,2).

The problem of quantification of uncertainty that has been considered in many fields such as prediction, prediction, and machine learning, has recently received much attention. Although some work focused on uncertainty quantification as a convex optimization problem, others focus on quantification of uncertainty as a multivariate regression problem, and have been shown to be NP-hard. In this paper we provide two theoretical results on the problem of quantification of uncertainty that is NP-hard. The first leads to the unification of the quantification of uncertainty problem into two univariate optimization problems: one where the output of the regression algorithm is a continuous point-dependent probability distribution, and the other where the output of the regression algorithm is an undirected graphical model. We demonstrate both the benefits and limitations of the two optimization problems in a unified framework and propose an effective framework for quantifying uncertainty for multivariate regression.

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Towards Large-Margin Cost-Sensitive Deep Learning

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  • Artificial neural networks for diabetic retinopathy diagnosis using iterative auto-inference and genetic programming

    On the Unnormalization of the Multivariate Marginal DistributionThe problem of quantification of uncertainty that has been considered in many fields such as prediction, prediction, and machine learning, has recently received much attention. Although some work focused on uncertainty quantification as a convex optimization problem, others focus on quantification of uncertainty as a multivariate regression problem, and have been shown to be NP-hard. In this paper we provide two theoretical results on the problem of quantification of uncertainty that is NP-hard. The first leads to the unification of the quantification of uncertainty problem into two univariate optimization problems: one where the output of the regression algorithm is a continuous point-dependent probability distribution, and the other where the output of the regression algorithm is an undirected graphical model. We demonstrate both the benefits and limitations of the two optimization problems in a unified framework and propose an effective framework for quantifying uncertainty for multivariate regression.


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