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Noisy problems, i.e., problems for which the function can return different results when it is called with the exact same parameter, such as independent episodes in reinforcement learning.These can include power systems (because some power plants have tunable continuous outputs and others have continuous or semicontinuous outputs) or tasks with neural networks that require simultaneously choosing the learning rate per layer, the weight decay per layer, and the types of nonlinearity per layer. Discrete, continuous, or mixed problems.Examples include architecture search for deep learning or other forms of design, as well as the parametrization of multitask networks. Partially separable problems, which can be addressed by considering several blocks of variables.Separable or rotated problems, including partially rotated problems.Ill-conditioned problems, which typically arise when trying to optimize several variables with very different dynamics (for example, dropout and learning rate without problem-specific rescaling).(For example, hyperparametrization of deep learning for language modeling.) Multimodal problems, such as those that have several minima.The gradient-free optimizations included in Nevergrad are used for a wide variety of ML problems, such as: Or, they can use well-known benchmarks to evaluate how a new derivative-free optimization method compares with the current state of the art. Now, using Nevergrad, AI developers can easily test many different methods on a particular ML problem and then compare results. Previously, using these algorithms often involved custom-built implementations that make it difficult or impossible to compare results from a wide range of state-of-the-art methods. Population control methods for noise management.The library includes a wide range of optimizers, such as: It’s helping to better tune ML models in place of parameter sweeping, for example. At FAIR, the Facebook Artificial Intelligence Research group, researchers are using Nevergrad in a variety of projects in reinforcement learning, image generation, and other areas. It also will help ML scientists find the best optimizer for specific use cases.
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The platform enables them to implement state-of-the-art algorithms and methods to compare performance in different settings. The library is now available and of immediate use as a toolbox for AI researchers and others whose work involves derivative-free optimization. It also includes testing and evaluation tools. Nevergrad offers an extensive collection of algorithms that do not require gradient computation and presents them in a standard ask-and-tell Python framework. To make this faster and easier, we have created and are now open-sourcing a Python3 library called Nevergrad. Most machine learning tasks - from natural language processing to image classification to translation and many others - rely on derivative-free optimization to tune parameters and/or hyperparameters in their models.