论文标题
部分可观测时空混沌系统的无模型预测
Tree DNN: A Deep Container Network
论文作者
论文摘要
多任务学习（MTL）表明了其在用户产品中的重要性，用于快速培训，数据效率，降低过度拟合等。MTL通过共享网络参数并同时培训网络来实现它。但是，如果每个任务都需要来自其他数据集的培训，则MTL不提供解决方案。为了解决陈述的问题，我们提出了一个名为Treednn的架构以及其培训方法。 Treednn可以同时使用多个数据集训练模型，其中树的每个分支可能需要不同的培训数据集。我们在结果中表明，Treednn提供了竞争性能，具有减少参数存储的ROM要求的优势，并通过在推理时仅加载特定的分支来提高系统的响应能力。
Multi-Task Learning (MTL) has shown its importance at user products for fast training, data efficiency, reduced overfitting etc. MTL achieves it by sharing the network parameters and training a network for multiple tasks simultaneously. However, MTL does not provide the solution, if each task needs training from a different dataset. In order to solve the stated problem, we have proposed an architecture named TreeDNN along with it's training methodology. TreeDNN helps in training the model with multiple datasets simultaneously, where each branch of the tree may need a different training dataset. We have shown in the results that TreeDNN provides competitive performance with the advantage of reduced ROM requirement for parameter storage and increased responsiveness of the system by loading only specific branch at inference time.
