text: 252,146,1247697430285447168,1324039292,Hanxiao Liu,"New paper: Evolving Normalization-Activation Layers. We use evolution to design new layers called EvoNorms, which outperform BatchNorm-ReLU on many tasks. A promising use of AutoML to discover fundamental ML building blocks. <LINK> Joint work with @DeepMind <LINK> Key ideas: (1) to start from low-level primitives, and (2) to evolve the layers' generalization over multiple architectures. EvoNorms achieved promising results on ResNets, MobileNets, EfficientNets, Mask R-CNN and BigGAN. Pseudocode available in the appendix. <LINK>",http://arxiv.org/abs/2004.02967,"Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods. To address the challenge, we develop efficient rejection protocols to quickly filter out candidate layers that do not work well. We also use multi-objective evolution to optimize each layer's performance across many architectures to prevent overfitting. Our method leads to the discovery of EvoNorms, a set of new normalization-activation layers with novel, and sometimes surprising structures that go beyond existing design patterns. For example, some EvoNorms do not assume that normalization and activation functions must be applied sequentially, nor need to center the feature maps, nor require explicit activation functions. Our experiments show that EvoNorms work well on image classification models including ResNets, MobileNets and EfficientNets but also transfer well to Mask R-CNN with FPN/SpineNet for instance segmentation and to BigGAN for image synthesis, outperforming BatchNorm and GroupNorm based layers in many cases. ",Evolving Normalization-Activation Layers,2,"['New paper: Evolving Normalization-Activation Layers.\n\nWe use evolution to design new layers called EvoNorms, which outperform BatchNorm-ReLU on many tasks. A promising use of AutoML to discover fundamental ML building blocks.\n\n<LINK>\n\nJoint work with @DeepMind <LINK>', ""Key ideas: (1) to start from low-level primitives, and (2) to evolve the layers' generalization over multiple architectures. EvoNorms achieved promising results on ResNets, MobileNets, EfficientNets, Mask R-CNN and BigGAN. Pseudocode available in the appendix. https://t.co/W2jwvKJwoQ""]",20,04,532