论文标题
具有二元激活功能的深神经网络的整数编程方法
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
论文作者
论文摘要
我们研究具有二元激活函数（BDNN）的深神经网络，即激活函数仅具有两个状态。我们表明，可以将BDNN重新构成混合企业线性程序，可以通过经典的整数编程求解器来解决全球最优性。此外，提出了一种启发式溶液算法，我们在数据不确定性下研究模型，采用两阶段的强大优化方法。我们在随机和真实数据集上实施了方法，并表明BDNN的启发式版本在威斯康星州乳腺癌数据集上的经典深度神经网络的表现，同时在随机数据上表现较差。
We study deep neural networks with binary activation functions (BDNN), i.e. the activation function only has two states. We show that the BDNN can be reformulated as a mixed-integer linear program which can be solved to global optimality by classical integer programming solvers. Additionally, a heuristic solution algorithm is presented and we study the model under data uncertainty, applying a two-stage robust optimization approach. We implemented our methods on random and real datasets and show that the heuristic version of the BDNN outperforms classical deep neural networks on the Breast Cancer Wisconsin dataset while performing worse on random data.
