估计深神经网络（DNN）的概括误差（GE）是一项重要任务，通常依赖于持有数据的可用性。基于单个训练集更好地预测GE的能力可能会产生总体DNN设计原则，以减少对试用和错误的依赖以及其他绩效评估优势。为了寻找与GE相关的数量，我们使用无限宽度DNN限制到绑定的MI，研究了输入和最终层表示之间的相互信息（MI）。现有的基于输入压缩的GE绑定用于链接MI和GE。据我们所知，这代表了该界限的首次实证研究。为了实证伪造理论界限，我们发现它通常对于表现最佳模型而言通常很紧。此外，它在许多情况下检测到训练标签的随机化，反映了测试时间扰动的鲁棒性，并且只有很少的培训样本就可以很好地工作。考虑到输入压缩是广泛适用的，可以在信心估算MI的情况下，这些结果是有希望的。

Deploying machine learning models in production may allow adversaries to infer sensitive information about training data. There is a vast literature analyzing different types of inference risks, ranging from membership inference to reconstruction attacks. Inspired by the success of games (i.e., probabilistic experiments) to study security properties in cryptography, some authors describe privacy inference risks in machine learning using a similar game-based style. However, adversary capabilities and goals are often stated in subtly different ways from one presentation to the other, which makes it hard to relate and compose results. In this paper, we present a game-based framework to systematize the body of knowledge on privacy inference risks in machine learning.

Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality inspection (VQI) tailored for real-world manufacturing environments. To enable automated VQI on the edge computing within the fully-integrated VQI system, a highly compact deep anti-aliased attention condenser neural network (which we name LightDefectNet) tailored specifically for light guide plate surface defect detection in resource-constrained scenarios was created via machine-driven design exploration with computational and "best-practices" constraints as well as L_1 paired classification discrepancy loss. Experiments show that LightDetectNet achieves a detection accuracy of ~98.2% on the LGPSDD benchmark while having just 770K parameters (~33X and ~6.9X lower than ResNet-50 and EfficientNet-B0, respectively) and ~93M FLOPs (~88X and ~8.4X lower than ResNet-50 and EfficientNet-B0, respectively) and ~8.8X faster inference speed than EfficientNet-B0 on an embedded ARM processor. As such, the proposed deep learning-driven workflow, integrated with the aforementioned LightDefectNet neural network, is highly suited for high-throughput, high-performance light plate surface VQI within real-world manufacturing environments.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e.g., offering a discount to a customer) to increase the probability of a desired case outcome (e.g., a customer making a purchase). The backbone of a prescriptive process monitoring method is an intervention policy, which determines for which cases and when an intervention should be executed. Existing methods in this field rely on predictive models to define intervention policies; specifically, they consider policies that trigger an intervention when the estimated probability of a negative outcome exceeds a threshold. However, the probabilities computed by a predictive model may come with a high level of uncertainty (low confidence), leading to unnecessary interventions and, thus, wasted effort. This waste is particularly problematic when the resources available to execute interventions are limited. To tackle this shortcoming, this paper proposes an approach to extend existing prescriptive process monitoring methods with so-called conformal predictions, i.e., predictions with confidence guarantees. An empirical evaluation using real-life public datasets shows that conformal predictions enhance the net gain of prescriptive process monitoring methods under limited resources.

Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts.

Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However, scaling Bayesian inference to large architectures is challenging and requires restrictive approximations. Monte Carlo Dropout has been widely used as a relatively cheap way for approximate Inference and to estimate uncertainty with deep neural networks. Traditionally, the dropout mask is sampled independently from a fixed distribution. Recent works show that the dropout mask can be viewed as a latent variable, which can be inferred with variational inference. These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation. In this work, we propose GFlowOut to address these issues. GFlowOut leverages the recently proposed probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks. We empirically demonstrate that GFlowOut results in predictive distributions that generalize better to out-of-distribution data, and provide uncertainty estimates which lead to better performance in downstream tasks.

人工智能（AI）是21世纪最有前途的技术之一，对社会和经济产生了明显影响。通过这项工作，我们简要概述了全球趋势，行业应用以及我们在工业和学术界的国际经验和工作中的精选用例。目的是提出全球和地区的积极实践，并就将B＆H定位在全球AI场景中定位的现实目标和机会提供明智的意见。

从分区的输入空间中生成不安全的子要求，以支持验证引导的测试案例以正式验证黑盒模型，这对研究人员来说是一个具有挑战性的问题。搜索空间的大小使详尽的搜索在计算上是不切实际的。本文调查了一种元热疗法方法，以在分区的输入空间中搜索不安全的候选子要求。我们提出了一种负选择算法（NSA），用于识别给定安全性质内候选人的不安全区域。 NSA算法的元效力能力使得在验证这些区域的一部分时估算庞大的不安全区域成为可能。我们利用分区空间的并行执行来生产安全区域。基于安全区域的先验知识的NSA用于识别候选不安全区域，然后使用Marabou框架来验证NSA结果。我们的初步实验和评估表明，该程序在用Marabou框架验证的高精度验证时发现候选人不安全的子裁定。

对复杂建筑环境的结构监测通常在设计，实验室测试和实际建筑参数之间遭受不匹配。此外，现实世界中的结构识别问题遇到了许多挑战。例如，缺乏准确的基线模型，高维度和复杂的多元部分微分方程（PDE）在训练和学习常规数据驱动算法方面遇到了重大困难。本文通过增强使用神经网络来控制结构动力学的PDE来探讨一个称为Neuralsi的新框架，以供结构识别。我们的方法试图从管理方程式估算非线性参数。我们考虑具有两个未知参数的非线性光束的振动，一个参数代表几何和材料变化，另一种代表主要通过阻尼捕获系统中的能量损失。参数估计的数据是从有限的一组测量值中获得的，这有利于在结构健康监测中的应用，其中通常未知现有结构的确切状态，并且只能在现场收集有限的数据样本。也可以使用已识别的结构参数在标准和极端条件下训练有素的模型。我们与纯数据驱动的神经网络和其他经典物理信息的神经网络（PINN）进行了比较。我们的方法将位移分布中的插值和外推误差降低了基线上的两到五个数量级。代码可从https://github.com/human-analysis/naural-scruptural-isendification获得。