论文标题
通过顺序的蒙特卡洛辍学适应神经模型
Adapting Neural Models with Sequential Monte Carlo Dropout
论文作者
论文摘要
适应不断变化的环境和设置的能力对于在动态和非结构化环境中起作用或与具有不同能力或偏好的人一起工作至关重要。这项工作介绍了一种非常简单有效的方法，用于调整神经模型以响应不断变化的设置。我们首先使用辍学训练标准网络，该网络类似于学习预测模型的集合或对预测的分布。在运行时，我们使用粒子过滤器来维持辍学掩模的分布，以使神经模型以在线方式更改设置。实验结果表明，在需要在线和预测预测的控制问题中的性能提高了，并在人类行为建模任务中展示了推断面具的解释性。
The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities or preferences. This work introduces an extremely simple and effective approach to adapting neural models in response to changing settings. We first train a standard network using dropout, which is analogous to learning an ensemble of predictive models or distribution over predictions. At run-time, we use a particle filter to maintain a distribution over dropout masks to adapt the neural model to changing settings in an online manner. Experimental results show improved performance in control problems requiring both online and look-ahead prediction, and showcase the interpretability of the inferred masks in a human behaviour modelling task for drone teleoperation.
