The authors build a nearly end-to-end text-to-speech (TTS) synthesis pipeline, resulting in high-fidelity natural-sounding speech approaching state-of-the-art TTS systems.
This paper presents a block-based deep neural architecture for univariate time series point forecasting that is similar in its philosophy to very deep models (e.g. ResNet) used in more common deep learning applications such as image recognition. Furthermore, the authors demonstrate how their approach can be used to build predictive models that are interpretable.
This paper uses deep sequence-to-sequence models to perform integration and solve differential equations in symbolic form.