我们通过Pac-Bayes概括界的镜头研究冷后效应。我们认为，在非反应环境中，当训练样本的数量相对较小时，应考虑到冷后效应的讨论，即大概贝叶斯推理并不能容易地提供对样本外数据的性能的保证。取而代之的是，通过泛化结合更好地描述了样本外误差。在这种情况下，我们探讨了各种推理与PAC-Bayes目标的ELBO目标之间的联系。我们注意到，虽然Elbo和Pac-Bayes目标相似，但后一个目标自然包含温度参数$ \ lambda $，不限于$ \ lambda = 1 $。对于回归和分类任务，在各向同性拉普拉斯与后部的近似值的情况下，我们展示了这种对温度参数的PAC-bayesian解释如何捕获冷后效应。

The study of feature propagation at initialization in neural networks lies at the root of numerous initialization designs. An assumption very commonly made in the field states that the pre-activations are Gaussian. Although this convenient Gaussian hypothesis can be justified when the number of neurons per layer tends to infinity, it is challenged by both theoretical and experimental works for finite-width neural networks. Our major contribution is to construct a family of pairs of activation functions and initialization distributions that ensure that the pre-activations remain Gaussian throughout the network's depth, even in narrow neural networks. In the process, we discover a set of constraints that a neural network should fulfill to ensure Gaussian pre-activations. Additionally, we provide a critical review of the claims of the Edge of Chaos line of works and build an exact Edge of Chaos analysis. We also propose a unified view on pre-activations propagation, encompassing the framework of several well-known initialization procedures. Finally, our work provides a principled framework for answering the much-debated question: is it desirable to initialize the training of a neural network whose pre-activations are ensured to be Gaussian?

贝叶斯神经网络与高斯过程之间的联系在过去几年中获得了很多关注，其中隐藏的单位在层宽趋于无穷大时收敛到高斯过程限制。支撑此结果是隐藏单元在无限宽度限制中变得独立。我们的宗旨是在实际有限宽度贝叶斯神经网络中阐明隐藏的单位依赖性质。除了理论结果之外，我们除了对隐藏的单位依赖性属性的深度和宽度影响。

We propose an efficient method to learn both unstructured and structured sparse neural networks during training, using a novel generalization of the sparse envelope function (SEF) used as a regularizer, termed {\itshape{group sparse envelope function}} (GSEF). The GSEF acts as a neuron group selector, which we leverage to induce structured pruning. Our method receives a hardware-friendly structured sparsity of a deep neural network (DNN) to efficiently accelerate the DNN's evaluation. This method is flexible in the sense that it allows any hardware to dictate the definition of a group, such as a filter, channel, filter shape, layer depth, a single parameter (unstructured), etc. By the nature of the GSEF, the proposed method is the first to make possible a pre-define sparsity level that is being achieved at the training convergence, while maintaining negligible network accuracy degradation. We propose an efficient method to calculate the exact value of the GSEF along with its proximal operator, in a worst-case complexity of $O(n)$, where $n$ is the total number of groups variables. In addition, we propose a proximal-gradient-based optimization method to train the model, that is, the non-convex minimization of the sum of the neural network loss and the GSEF. Finally, we conduct an experiment and illustrate the efficiency of our proposed technique in terms of the completion ratio, accuracy, and inference latency.

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the 'ground-truth' label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

在许多临床背景下，检测所有病变对于评估疾病活动至关重要。尽管获取分割标签的耗时性，但标准方法仍将病变检测作为分割问题。在本文中，我们提出了一种仅依赖点标签的病变检测方法。我们的模型通过热图回归训练，可以以概率方式检测可变数量的病变。实际上，我们提出的后处理方法提供了一种直接估计病变存在不确定性的可靠方法。GAD病变检测的实验结果表明，与昂贵的分割标签的培训相比，我们的基于点的方法具有竞争性。最后，我们的检测模型为分割提供了合适的预训练。仅在17个细分样本上进行微调时，我们实现了与完整数据集的培训相当的性能。

发现预测未来疾病结果的患者特定成像标记可以帮助我们更好地了解疾病进化的个体水平异质性。实际上，可以在医学实践中采用的可以提供数据驱动的个性化标记的深度学习模型。在这项工作中，我们证明了数据驱动的生物标志物发现可以通过反事实综合过程来实现。我们展示了如何使用深层的条件生成模型来扰动基线图像中的局部成像特征，这些图像与特定于受试者的未来疾病进化有关，并导致反事实图像有望具有不同的未来结果。因此，候选生物标志物是由于检查了此过程中受到干扰的一组功能而产生的。通过对大型多扫描仪多中心多发性硬化症（MS）临床试验磁共振成像（MRI）数据集（RRMS）患者数据集（RRMS）患者数据集进行的几项实验，我们证明我们的模型会产生反面的反面事件，并具有成像变化反映了建立的临床标记的特征，可预测人群水平的未来MRI病变活性。其他定性结果表明，我们的模型有可能发现未来活动的新颖和主题的预测标记。

大型，注释的数据集在医学图像分析中不广泛使用，这是由于时间，成本和标记大型数据集相关的挑战。未标记的数据集更容易获取，在许多情况下，专家可以为一小部分图像提供标签是可行的。这项工作提出了一个信息理论的主动学习框架，该框架可以根据评估数据集中最大化预期信息增益（EIG）来指导未标记池的最佳图像选择。实验是在两个不同的医学图像分类数据集上进行的：多类糖尿病性视网膜病变量表分类和多级皮肤病变分类。结果表明，通过调整EIG来说明班级不平衡，我们提出的适应预期信息增益（AEIG）的表现优于几个流行的基线，包括基于多样性的核心和基于不确定性的最大熵抽样。具体而言，AEIG仅占总体表现的95％，只有19％的培训数据，而其他活跃的学习方法则需要约25％。我们表明，通过仔细的设计选择，我们的模型可以集成到现有的深度学习分类器中。

自动生物医学图像分析的领域至关重要地取决于算法验证的可靠和有意义的性能指标。但是，当前的度量使用通常是不明智的，并且不能反映基本的域名。在这里，我们提出了一个全面的框架，该框架指导研究人员以问题意识的方式选择绩效指标。具体而言，我们专注于生物医学图像分析问题，这些问题可以解释为图像，对象或像素级别的分类任务。该框架首先编译域兴趣 - 目标结构 - ，数据集和算法与输出问题相关的属性的属性与问题指纹相关，同时还将其映射到适当的问题类别，即图像级分类，语义分段，实例，实例细分或对象检测。然后，它指导用户选择和应用一组适当的验证指标的过程，同时使他们意识到与个人选择相关的潜在陷阱。在本文中，我们描述了指标重新加载推荐框架的当前状态，目的是从图像分析社区获得建设性的反馈。当前版本是在由60多个图像分析专家的国际联盟中开发的，将在社区驱动的优化之后公开作为用户友好的工具包提供。