论文标题
关于辍学训练的收敛和概括
On Convergence and Generalization of Dropout Training
论文作者
论文摘要
我们研究具有整流线性单元（RELU）激活的两层神经网络中的辍学。在轻度的过度参数化并假设限制内核可以以正差分开数据分布，我们表明具有逻辑损失的辍学训练可实现$ O（1/ε）$迭代中的测试错误中的$ε$ -Suboptimality。
We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the data distribution with a positive margin, we show that dropout training with logistic loss achieves $ε$-suboptimality in test error in $O(1/ε)$ iterations.
