Title: NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks. Abstract: Siamese networks are one of the most trending methods to achieve unsupervised visual representation learning. Meanwhile, Neural Architecture Search (NAS) is becoming increasingly important as a technique to discover efficient deep learning architectures. In this article, we present NASiam, a novel approach that uses for the first time differentiable NAS to improve the Multilayer Perceptron projector and predictor (encoder/predictor pair) architectures inside Siamese networks frameworks while preserving the simplicity of previous baselines. We show that these new architectures allow backbone convolutional models to learn strong representations efficiently. NASiam reaches competitive performance in both small-scale (CIFAR) and large-scale (ImageNet) image classification datasets. We discuss the composition of the NAS-discovered architectures and emit hypotheses on why they manage to prevent collapsing behavior.