We present a novel semi-supervised learning framework that intelligently
leverages the consistency regularization between the model's predictions from
two strongly-augmented views of an image, weighted by a confidence of
pseudo-label, dubbed ConMatch. While the latest semi-supervised learning
methods use weakly- and strongly-augmented views of an image to define a
directional consistency loss, how to define such direction for the consistency
regularization between two strongly-augmented views remains unexplored. To
account for this, we present novel confidence measures for pseudo-labels from
strongly-augmented views by means of weakly-augmented view as an anchor in
non-parametric and parametric approaches. Especially, in parametric approach,
we present, for the first time, to learn the confidence of pseudo-label within
the networks, which is learned with backbone model in an end-to-end manner. In
addition, we also present a stage-wise training to boost the convergence of
training. When incorporated in existing semi-supervised learners, ConMatch
consistently boosts the performance. We conduct experiments to demonstrate the
effectiveness of our ConMatch over the latest methods and provide extensive
ablation studies. Code has been made publicly available at
this https URL

本文介绍了一种名为 ConMatch 的新型半监督学习框架，它在图像的两个强大的增强视图的模型预测之间智能地利用一致性正则化，权重由伪标签的置信度确定。该方法通过弱增强视图作为锚点，提出了新的伪标签置信度度量方法，包括非参数方法和参数方法。特别是，在参数方法中，我们首次提出在网络内学习伪标签置信度，可以端到端地与骨干模型一起训练。在现有的半监督学习中加入 ConMatch 可以持续提高性能，本文通过实验和大量的消融研究证明了 ConMatch 的有效性。

ConMatch: 带置信度引导的连贯性正则化半监督学习

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency  Regularization

Learning the principal eigenfunctions of an integral operator defined by a
kernel and a data distribution is at the core of many machine learning
problems. Traditional nonparametric solutions based on the Nystr{\"o}m formula
suffer from scalability issues. Recent work has resorted to a parametric
approach, i.e., training neural networks to approximate the eigenfunctions.
However, the existing method relies on an expensive orthogonalization step and
is difficult to implement. We show that these problems can be fixed by using a
new series of objective functions that generalizes the
EigenGame~\citep{gemp2020eigengame} to function space. We test our method on a
variety of supervised and unsupervised learning problems and show it provides
accurate approximations to the eigenfunctions of polynomial, radial basis,
neural network Gaussian process, and neural tangent kernels. Finally, we
demonstrate our method can scale up linearised Laplace approximation of deep
neural networks to modern image classification datasets through approximating
the Gauss-Newton matrix. Code is available at
https://github.com/thudzj/neuraleigenfunction.

论文研究了一个新的、广义的针对函数空间的目标函数，应用于机器学习中核函数和数据分布定义的积分算子的主特征向量的训练。该方法通过近似高斯牛顿矩阵来实现线性化的深度神经网络在现代图像分类数据集上的扩展，能够提供对多元函数的准确逼近。

NeuralEF: 通过深度神经网络解构卷积核

NeuralEF: Deconstructing Kernels by Deep Neural Networks

We describe a new approach to estimating relative risks in time-to-event
prediction problems with censored data in a fully parametric manner. Our
approach does not require making strong assumptions of constant proportional
hazard of the underlying survival distribution, as required by the
Cox-proportional hazard model. By jointly learning deep nonlinear
representations of the input covariates, we demonstrate the benefits of our
approach when used to estimate survival risks through extensive experimentation
on multiple real world datasets with different levels of censoring. We further
demonstrate advantages of our model in the competing risks scenario. To the
best of our knowledge, this is the first work involving fully parametric
estimation of survival times with competing risks in the presence of censoring.

本文介绍一种新的参数方法来评估具有截尾数据的时间事件预测问题中的相对风险，通过联合学习输入协变量的深度非线性表示，我们展示了我们的方法在多个不同程度的截尾实际世界数据集上估算生存风险的优势，并证明我们的模型在竞争风险情景中的优势。据我们所知，这是在存在截尾时进行全参数生存时间与竞争风险估计的第一项研究。