在源代码中自动定位易受攻击的陈述至关重要，以确保软件安全性和缓解开发人员的调试工作。这在当今软件生态系统中变得更加重要，其中易受攻击的代码可以在像GitHub这样的软件存储库中轻松且无意中流动。在这类数百万的代码行中，传统的静态和动态方法争取缩放。虽然基于机器学习的方法在这样的设置中看起来很有希望，但大多数工作都在较高的粒度下检测到脆弱的代码 - 在方法或文件级别。因此，开发人员仍然需要检查大量代码以找到需要修复的弱势陈述。本文提出了一种新的集合学习方法来定位脆弱的陈述。我们的模型结合了基于图形的基于序列的神经网络，以成功捕获程序图的本地和全局上下文，并有效地了解代码语义和易受攻击的模式。为了研究天鹅绒的效果，我们使用了一个现成的合成数据集和最近发布的现实世界数据集。在静态分析设置中，未提前检测到易受攻击功能，Velvet可以实现4.5倍的性能，而不是真实世界数据上的基线静态分析仪。对于孤立的漏洞本地化任务，在我们假设特定漏洞声明未知的同时知道函数的漏洞，我们将天鹅绒与几个神经网络进行比较，这些内部网络也参加了本地和全局代码背景。天鹅绒分别达到99.6％和43.6％的13.6％，分别在合成数据和现实世界数据上实现了高精度，优于基线深度学习模型5.3-29.0％。

源代码的AI建模理解任务一直在取得重大进展，并正在生产开发管道中采用。然而，可靠性问题，特别是模型是否实际上是学习源代码的任务相关方面，正在提出。虽然最近的模型探测方法已经观察到许多用于代码模型中的信号意识缺乏信号意识，即，未捕获任务相关信号的模型，它们不提供解决问题的解决方案。在本文中，我们探索了数据驱动的方法来提高模型的模型“信号意识：1）我们将SE概念与课程学习的AI技术相结合; 2）通过自定义Delta调试以生成简化的信号保留程序，将它们纳入AI模型，将SE辅助纳入AI模型。通过我们的技术，我们在模型信号意识方面取得了高达4.8倍的提高。使用代码复杂性的概念，我们进一步从数据集的角度介绍了一种新颖的学习内省方法。

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.

We present a framework for ranking images within their class based on the strength of spurious cues present. By measuring the gap in accuracy on the highest and lowest ranked images (we call this spurious gap), we assess spurious feature reliance for $89$ diverse ImageNet models, finding that even the best models underperform in images with weak spurious presence. However, the effect of spurious cues varies far more dramatically across classes, emphasizing the crucial, often overlooked, class-dependence of the spurious correlation problem. While most spurious features we observe are clarifying (i.e. improving test-time accuracy when present, as is typically expected), we surprisingly find many cases of confusing spurious features, where models perform better when they are absent. We then close the spurious gap by training new classification heads on lowly ranked (i.e. without common spurious cues) images, resulting in improved effective robustness to distribution shifts (ObjectNet, ImageNet-R, ImageNet-Sketch). We also propose a second metric to assess feature reliability, finding that spurious features are generally less reliable than non-spurious (core) ones, though again, spurious features can be more reliable for certain classes. To enable our analysis, we annotated $5,000$ feature-class dependencies over {\it all} of ImageNet as core or spurious using minimal human supervision. Finally, we show the feature discovery and spuriosity ranking framework can be extended to other datasets like CelebA and WaterBirds in a lightweight fashion with only linear layer training, leading to discovering a previously unknown racial bias in the Celeb-A hair classification.

Recommender systems are ubiquitous in most of our interactions in the current digital world. Whether shopping for clothes, scrolling YouTube for exciting videos, or searching for restaurants in a new city, the recommender systems at the back-end power these services. Most large-scale recommender systems are huge models trained on extensive datasets and are black-boxes to both their developers and end-users. Prior research has shown that providing recommendations along with their reason enhances trust, scrutability, and persuasiveness of the recommender systems. Recent literature in explainability has been inundated with works proposing several algorithms to this end. Most of these works provide item-style explanations, i.e., `We recommend item A because you bought item B.' We propose a novel approach, RecXplainer, to generate more fine-grained explanations based on the user's preference over the attributes of the recommended items. We perform experiments using real-world datasets and demonstrate the efficacy of RecXplainer in capturing users' preferences and using them to explain recommendations. We also propose ten new evaluation metrics and compare RecXplainer to six baseline methods.

Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.

视觉问题回答（VQA）是一项多模式的任务，涉及从输入图像中回答问题，以语义了解图像的内容并以自然语言回答。由于VQA系统回答的问题范围，使用VQA进行灾难管理是一项重要的研究。但是，主要的挑战是评估受影响地区的标签产生的延迟。为了解决这个问题，我们部署了预先训练的剪辑模型，该模型在视觉图像对中进行了训练。但是，我们从经验上看到该模型的零击性能差。因此，我们相反，我们使用此模型中的文本和图像的预训练嵌入，进行我们的监督培训，并超过Floodnet数据集上的先前最新结果。我们将其扩展到持续的设置，这是一种更现实的情况。我们解决了使用各种经验重播方法的灾难性遗忘的问题。我们的培训运行可在以下网址提供：https：//wandb.ai/compyle/continual_vqa_final

混合整数程序（MIP）通常通过分支结合算法解决。最近，学会模仿专家强的分支启发式的快速近似，由于它成功地减少了解决MIP的运行时间，因此引起了人们的关注。但是，现有的学习与分支方法假设整个培训数据都可以在一次培训中获得。这个假设通常不正确，如果随着时间的推移以连续的方式提供培训数据，现有技术会遭受灾难性遗忘。在这项工作中，我们研究了迄今未开发的终身学习范式，以在混合整数程序上分支。为了减轻灾难性的遗忘，我们提出了Limip，该limip是由以两部分图的形式对MIP实例进行建模的想法，我们使用双方图形注意力网络将其映射到嵌入式空间。这种丰富的嵌入空间避免了通过应用知识蒸馏和弹性重量巩固的灾难性遗忘，其中我们学习参数的关键是保持疗效，因此受到保护，免受明显的漂移。我们评估了一系列NP硬性问题的利润，并确定与现有基线相比，在面对终身学习时，Limip的速度高达50％。

深度学习网络已在各种应用中表现出高性能，例如图像分类，语音识别和自然语言处理。但是，存在使用对抗攻击所利用的主要漏洞。对抗性攻击通过稍微稍微更改输入图像，使其对肉眼几乎无法检测到图像，但导致网络的分类非常不同。本文探讨了使用两种类型的体系结构：MobileNetV3和Resnet50探讨图像分割DeepLabV3模型上预计的梯度下降（PGD）攻击和自适应面膜分割攻击（ASMA），发现PGD在更改分割方面非常一致它的目标虽然ASMA对多类目标的概括不那么有效。然而，这种攻击的存在使所有图像分类深度学习网络处于剥削的危险之中。

减少甲烷排放对于缓解全球变暖至关重要。为了将甲烷排放归因于其来源，有必要综合的甲烷源基础设施数据集。深入学习远程感知的图像的最新进展有可能识别甲烷源的位置和特征，但是缺乏公开可用的数据，可以使机器学习研究人员和从业人员能够构建自动映射方法。为了帮助填补这一空白，我们在美国构建了一个称为Meter-ML的多传感器数据集，该数据集包含86,625个地理参考的NAIP，Sentinel-1和Sentinel-2图像，并在美国标记为有甲烷源设施，包括甲烷源设施，包括集中动物喂养操作，，，，，，，包括浓缩动物喂养操作，煤矿，垃圾填埋场，天然气加工厂，炼油厂和石油末端以及废水处理厂。我们尝试各种模型，以利用不同的空间分辨率，空间足迹，图像产品和光谱带。我们发现，我们的最佳模型在确定浓缩动物喂养操作的精确召回曲线下达到了一个面积，在专家标签的测试集上，用于识别浓缩动物饲养操作，用于油炼油厂和石油末端0.821，这表明有可能进行大规模映射。我们在https://stanfordmlgroup.github.io/projects/meter-ml/上免费提供仪表-ML，以支持自动化甲烷源映射的未来工作。