论文标题
分析连续时间系统的深度学习方法
A Deep Learning Approach to Analyzing Continuous-Time Systems
论文作者
论文摘要
科学家经常使用观察时间序列数据来研究复杂的自然过程，但是回归分析通常会假设简单的动态。深度学习的最新进展已经对复杂过程模型的性能产生了令人震惊的改进，但是深度学习通常不用于科学分析。在这里，我们表明，深度学习可用于分析复杂的过程，提供灵活的功能近似，同时保持可解释性。我们的方法放宽了标准简化的假设（例如，线性，平稳性和同质性），这些假设对于许多自然系统来说是不可信的，并且可能会严重影响数据的解释。我们将模型评估为增量的人类语言处理，这是一个具有复杂连续动态的领域。我们证明了对行为和神经成像数据的实质性改进，我们表明我们的模型可以在探索性分析中发现新型模式，对确认性分析中各种混杂的控制，并打开了难以研究的研究问题。
Scientists often use observational time series data to study complex natural processes, but regression analyses often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to the performance of models of complex processes, but deep learning is generally not used for scientific analysis. Here we show that deep learning can be used to analyze complex processes, providing flexible function approximation while preserving interpretability. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many natural systems and may critically affect the interpretation of data. We evaluate our model on incremental human language processing, a domain with complex continuous dynamics. We demonstrate substantial improvements on behavioral and neuroimaging data, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions that are otherwise hard to study.
