Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system's structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns of change cannot be modeled using simple and predefined metrics, and (2) changes can occur gradually, at multiple time-scales. To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales. Our proposed method, pyramid recurrent neural network (PRNN), is designed to be scale-invariant, by incorporating wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRNN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel timescales that have not been seen in training.
1. This papers leverages the concept of wavelet transform within a deep architecture to solve the classic problem (especially for wavelet analysis) of change point detection. The authors do a reasonably comprehensive job of demonstrating the efficacy of the proposed framework using various synthetic and real data sets with both gradual and abrupt changes
2. The concept of pyramid network idea is not really new, in the context of CNN it has been established quite well. The paper should highlight this fact by citing papers such as "Lin, Tsung-Yi, et al. "Feature Pyramid Networks for Object Detection." CVPR. Vol. 1. No. 2. 2017." 
3. Involving wavelet transforms in deep nets have been done before. This paper attempts to learn wavelet transform parameters by involving them as trainable layers. But even this kind of idea is also emerging in the community. Papers such as "Fujieda, Shin, Kohei Takayama, and Toshiya Hachisuka. "Wavelet Convolutional Neural Networks." arXiv preprint arXiv:1805.08620 (2018)" need to be discussed in this context. 
4. The biggest issue in my mind is that I feel "Chung et al 2016" is still a very similar framework as the proposed one. While authors argue that it uses more like CNN architecture and the proposed method may pick up the multi-scale features better, comparison with this seems to be most appropriate. This will also clearly identify the benefits of the wavelet structure to the filters and multi-resolution analysis approaches.
5. RCNN term has been used for CNN+RNN architecture. This may not be a good terminology to use since RCNN is a very popular term referring to Region based CNN for detection and localization purposes.
6. AUC metric, I believe is the - area under ROC curve, this needs to be spelled out, how it is computed? at least in the Appendix
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Appreciate the authors' rebuttal, updated my score.