ﻻ يوجد ملخص باللغة العربية
High levels of sparsity and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data. While most methods tackle each problem separately, our proposed approach handles both in conjunction, while imposing fewer assumptions on the data. In this work, we propose leveraging a self-supervised learning method, specifically Autoregressive Predictive Coding (APC), to learn relevant hidden representations of time series data in the context of both missing data and class imbalance. We apply APC using either a GRU or GRU-D encoder on two real-world datasets, and show that applying one-step-ahead prediction with APC improves the classification results in all settings. In fact, by applying GRU-D - APC, we achieve state-of-the-art AUPRC results on the Physionet benchmark.
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on the assumption that node samples for different classes are balanced, while for many real-world
While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work, we investigate the transferability of state-of-the-art deep semi-supe
Learning to classify time series with limited data is a practical yet challenging problem. Current methods are primarily based on hand-designed feature extraction rules or domain-specific data augmentation. Motivated by the advances in deep speech pr
Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods. This is because meta-training needs to deal with more diverse distributions and train extra components such as context en
We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from there. Our methods address some overt problems such as chromatic aberrat