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模型选择：是否一定要选择参数量巨大的模型？如果需要更好的泛化能力，用于处理非单一的任务，例如对话，则可用选更大的模型；而对于单一明确的任务，则不一定越大越好，参数小一些的模型也能调教得很好。
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深度神经网络对计算和存储资源需求巨大，这给它们在边缘设备上的部署带来困难。最近，轻量级深度学习受到了极大关注，其目的是通过网络剪枝、低秩近似(LRA)、权重量化和网络架构转换(NAT)等压缩大型DNN模型。有工作基于矩阵逼近理论近似相对更低秩和稀疏的DNN模型的权重矩阵，从而得到一个轻量的紧凑模型。
