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The goal of the ongoing ComParE 2018 Atypical Affect sub-challenge is to recognize the emotional states of atypical individuals. In this work, we present three modeling methods under the end-to-end learning framework, namely CNN combined with extended features, CNN+RNN and ResNet, respectively. Furthermore, we investigate multiple data augmentation, balancing and sampling methods to further enhance the system performance. The experimental results show that data balancing and augmentation increase the unweighted accuracy (UAR) by 10% absolutely. After score level fusion, our proposed system achieves 48.8% UAR on the develop dataset.