Title: Sparse Hierarchical Table Ensemble. Abstract: Deep learning for tabular data is drawing increasing attention, with recent work attempting to boost the accuracy of neuron-based networks. However, when computational capacity is low as in Internet of Things (IoT), drone, or Natural User Interface (NUI) applications, such deep learning methods are deserted. We offer to enable deep learning capabilities using ferns (oblivious decision trees) instead of neurons, by constructing a Sparse Hierarchical Table Ensemble (S-HTE). S-HTE inference is dense at the beginning of the training process and becomes gradually sparse using an annealing mechanism, leading to an efficient final predictor. Unlike previous work with ferns, S-HTE learns useful internal representations, and it earns from increasing depth. Using a standard classification and regression benchmark, we show its accuracy is comparable to alternatives while having an order of magnitude lower computational complexity. Our PyTorch implementation is available at https://anonymous.4open.science/r/HTE_CTE-60EB/