Title: A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization. Abstract: Batch Normalization is an essential component of all state-of-the-art neural networks architectures. However, it introduces many practical issues so that in recent years a lot of research has been devoted to designing normalization-free architectures. In this paper, we show that weights initialization is key to train ResNet-like normalization-free networks. In particular, we propose a slight modification of the summation operation of a block output with the skip-connection branch so that the whole network is correctly initialized. We show that this modified architecture achieves competitive results on CIFAR-10 without additional regularization or algorithmic modifications.