论文标题
鲁宾大数据时代的贝叶斯神经网络用于分类任务
Bayesian Neural Networks for classification tasks in the Rubin big data era
论文作者
论文摘要
即将进行的调查，例如Vera C. Rubin天文台对时空的遗产调查（LSST），将在10年中每天晚上在天空中发现多达1000万个时变的消息来源。这些信息将在连续的流中传输到经纪人，这些经纪人将使用机器学习算法为各种科学案例选择最有希望的事件。我们研究贝叶斯神经网络（BNN）对这种类型的分类任务的好处和挑战。发现BNN是准确的分类器，它也提供了其他信息：它们量化了分类不确定性，可以利用该分类以更有效地分析该即将到来的数据。
Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will detect up to 10 million time-varying sources in the sky every night for ten years. This information will be transmitted in a continuous stream to brokers that will select the most promising events for a variety of science cases using machine learning algorithms. We study the benefits and challenges of Bayesian Neural Networks (BNNs) for this type of classification tasks. BNNs are found to be accurate classifiers which also provide additional information: they quantify the classification uncertainty which can be harnessed to analyse this upcoming data avalanche more efficiently.
