Title: AdaStride: Using Adaptive Strides in Sequential Data for Effective Downsampling. Abstract: The downsampling layer has been one of the most commonly used deep learning (DL) components in sequential data processing due to its several advantages. First, it improves the generalization performance of networks by acting as an information bottleneck, where it extracts task-relevant features and discards others. Second, it reduces data resolution allowing CNN layers to have larger receptive fields with smaller kernel sizes. Third, the reduced data resolution facilitates the use of Transformer networks in case of high-resolution data. Accordingly, there have been many studies on downsampling methods, but they have a limitation in that they apply the same downsampling ratio across a data instance. Using the same downsampling ratio uniformly for an entire data instance does not reflect the fact that the task-relevant information is not uniformly distributed in real data. In this paper, we introduce AdaStride, a downsampling method that can apply adaptively varying downsampling ratios across a sequential data instance given an overall downsampling ratio. Specifically, AdaStride learns to deploy adaptive strides in a sequential data instance. Therefore, it can preserve more information from task-relevant parts of a data instance by using smaller strides for those parts and larger strides for less relevant parts. To achieve this, we propose a novel training method called vector positioning that rearranges each time step of an input on a one-dimensional line segment without reordering, which is used to build an alignment matrix for the downsampling. In experiments conducted on three different tasks of audio classification, automatic speech recognition, and discrete representation learning, AdaStride outperforms other widely used standard downsampling methods showing its generality and effectiveness. In addition, we analyze how our AdaStride learns the effective adaptive strides to improve its performance in the tasks.