题目： Deep Isometric Learning for Visual Recognition
简介： 初始化，正则化和skip连接被认为是训练非常深的卷积神经网络并获得最新性能的三种必不可少的技术。 本文表明，无需规范化或skip连接的深层卷积网络也可以训练出在标准图像识别基准上获得令人惊讶的良好性能。 这是通过在初始化和训练过程中强制卷积内核接近等距来实现的，还可以通过使用ReLU的变体来实现等距变迁。 进一步的实验表明，如果与skip连接结合使用，则即使完全不进行正则化，此类近等距网络也可以达到ResNet在ImageNet与COCO数据集上相同的性能。
Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regression problems with Gaussian likelihoods, a GP model enjoys a posterior in closed form. However, identifying the posterior GP scales cubically with the number of training examples and requires to store all examples in memory. In order to overcome these obstacles, sparse GPs have been proposed that approximate the true posterior GP with pseudo-training examples. Importantly, the number of pseudo-training examples is user-defined and enables control over computational and memory complexity. In the general case, sparse GPs do not enjoy closed-form solutions and one has to resort to approximate inference. In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood. This paves the way for a powerful and versatile framework, where pseudo-training examples are treated as optimization arguments of the approximate posterior that are jointly identified together with hyperparameters of the generative model (i.e. prior and likelihood). The framework can naturally handle a wide scope of supervised learning problems, ranging from regression with heteroscedastic and non-Gaussian likelihoods to classification problems with discrete labels, but also multilabel problems. The purpose of this tutorial is to provide access to the basic matter for readers without prior knowledge in both GPs and VI. A proper exposition to the subject enables also access to more recent advances (like importance-weighted VI as well as interdomain, multioutput and deep GPs) that can serve as an inspiration for new research ideas.