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Forio Epicenter supports R, Python, Julia and other languages for optimization, machine learning, simulation, and other analytics techniques. The platform is enterprise-compatible with the ability to integrate with an organization’s existing IT infrastructure and tiered control for thousands of users.

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•LP: primal and dual simplex, sifting, parallel barrier, concurrent •QP: primal and dual simplex, parallel barrier •QCP: parallel barrier •Discrete optimization (MIP, MIQP, MIQCP) •Parallel branch-and-bound •Relaxations solved with continuous algorithms •Cutting planes and heuristics •General •Automatic presolve

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借助 Bayesian Optimization 自动搜索数据增强方法的探索. 借鉴Insight研究员的一个工作： AutoML数据增广. GitHub. 我探索了使用 Bayesian Optimization 自动搜索数据增强方法的实验。 在上面工作的基础上，我主要做了如下的修改： 1 将代理任务的网络由一个5层CNN改为mobilenet。

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Bayesian Optimization¶. Bayesian optimization is a powerful strategy for minimizing (or maximizing) objective functions that are costly to evaluate. It is an important component of automated machine learning toolboxes such as auto-sklearn, auto-weka, and scikit-optimize, where Bayesian optimization is used to select model hyperparameters.

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Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex … Johannes Kirschner , Mojmir Mutný , Nicole Hiller , Rasmus Ischebeck , Andreas Krause

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Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective function evaluations required for good ...