图形神经网络（GNNS）在许多图形挖掘任务中取得了巨大的成功，这些任务从消息传递策略中受益，该策略融合了局部结构和节点特征，从而为更好的图表表示学习。尽管GNN成功，并且与其他类型的深神经网络相似，但发现GNN容易受到图形结构和节点特征的不明显扰动。已经提出了许多对抗性攻击，以披露在不同的扰动策略下创建对抗性例子的GNN的脆弱性。但是，GNNS对成功后门攻击的脆弱性直到最近才显示。在本文中，我们披露了陷阱攻击，这是可转移的图形后门攻击。核心攻击原则是用基于扰动的触发器毒化训练数据集，这可以导致有效且可转移的后门攻击。图形的扰动触发是通过通过替代模型的基于梯度的得分矩阵在图形结构上执行扰动动作来生成的。与先前的作品相比，陷阱攻击在几种方面有所不同：i）利用替代图卷积网络（GCN）模型来生成基于黑盒的后门攻击的扰动触发器； ii）它产生了没有固定模式的样品特异性扰动触发器； iii）在使用锻造中毒训练数据集训练时，在GNN的背景下，攻击转移到了不同​​的GNN模型中。通过对四个现实世界数据集进行广泛的评估，我们证明了陷阱攻击使用四个现实世界数据集在四个不同流行的GNN中构建可转移的后门的有效性

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.

With the growth of residential rooftop PV adoption in recent decades, the problem of 1 effective layout design has become increasingly important in recent years. Although a number 2 of automated methods have been introduced, these tend to rely on simplifying assumptions and 3 heuristics to improve computational tractability. We demonstrate a fully automated layout design 4 pipeline that attempts to solve a more general formulation with greater geometric flexibility that 5 accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses 6 MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis 7 rather than imposing any predefined layouts. Our results demonstrate that although several common 8 heuristics are often effective, they may not be universally suitable due to complications resulting 9 from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the 10 literature and propose a potential new rule of thumb that may help improve rooftop solar energy 11 potential when shading effects are considered.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

在过去的十年中，图上的信号处理已成为一个非常活跃的研究领域。具体而言，使用从图形上构建的框架（例如图上的小波）在统计或深度学习中的应用数量显着增加。我们特别考虑通过数据驱动的小波紧密框架方法在图表上进行信号的情况。这种自适应方法基于使用Stein的无偏风险估计校准的阈值，该阈值适合于紧密框架表示。我们可以使用Chebyshev-Jackson多项式近似值将其扩展到大图，从而可以快速计算小波系数，而无需计算laplacian特征性组成。但是，紧密框架的过度本质将白噪声转化为相关的噪声。结果，转换噪声的协方差出现在确定的差异项中，因此需要计算和存储框架，从而导致大图的不切实际计算。为了估计这种协方差，我们基于零均值和单位方差随机变量的快速转换制定和分析蒙特卡洛策略。这种新的数据驱动的denoisisy方法可以在差异隐私中发现自然应用。从真实和模拟数据的大小变化图上进行了全面的性能分析。

诊断出红斑的偏头膜（EM）皮肤病变，使用深度学习技术的莱姆病最常见的早期症状可以有效预防长期并发症。现有的基于深度学习的EM识别的作品仅由于缺乏与相关患者数据相关的莱姆病相关图像的数据集，因此仅利用病变图像。医师依靠患者有关皮肤病变背景的信息来确认其诊断。为了协助深度学习模型，根据患者数据计算出的概率分数，这项研究引起了15位医生的意见。对于启发过程，准备了一份与EM相关的问题和可能的答案的问卷。医生为问题的不同答案提供了相对权重。我们使用基于高斯混合物的密度估计将医生评估转换为概率得分。为了引起概率模型验证，我们利用了形式的概念分析和决策树。引起的概率得分可用于使基于图像的深度学习莱姆病预扫描剂稳健。

概率时间序列预测在许多应用领域至关重要，例如零售，电子商务，金融或生物学。随着大量数据的增加，已经提出了许多神经架构为此问题。特别是，基于变压器的方法实现了现实世界基准的最先进的性能。然而，这些方法需要了解大量参数，这对培训此类模型的计算资源施加了高的内存要求。为了解决这个问题，我们介绍了一种新颖的双向时间卷积网络（Bitcn），该网络（Bitcn）需要比公共变换器的方法更少的参数较少的阶数。我们的模型结合了两个时间卷积网络（TCN）：第一个网络编码了时间序列的未来协变量，而第二网络编码过往观察和协变量。我们通过这两个网络联合估计输出分布的参数。四个现实世界数据集的实验表明，我们的方法与四个最先进的概率预测方法进行了表演，包括基于变压器的方法和Wavenet，在两点指标（Smape，NRMSE）以及A上大多数情况下的范围指标（定量损失百分位数）集。其次，我们证明我们的方法比基于变压器的方法所需的参数明显更少，这意味着模型可以培训更快，内存要求显着降低，因此降低了部署这些模型的基础架构成本。

我们考虑在自适应环境中的时间序列预测问题。我们专注于未知和潜在的时变噪声差异下的状态空间模型的推动。我们介绍了一个增强模型，其中差异在跟踪模式下表示为辅助高斯潜变量。随着差异是非负面的，选择转换并应用于这些潜在的变量。推断依赖于在线变分贝叶斯方法，这包括在每次步骤中最小化kullback-leibler发散。我们观察到Kallback-Leibler发散的最小值是卡尔曼滤波器的扩展，以考虑到方差不确定性。我们使用这些最佳递归更新设计一种名为Viking的新颖算法。对于辅助潜变量，我们使用的二阶界限，其最佳录取封闭式解决方案。合成数据的实验表明，Viking的表现良好，并且对拼盘进行了强大。

深度强化学习（RL）导致了许多最近和开创性的进步。但是，这些进步通常以培训的基础体系结构的规模增加以及用于训练它们的RL算法的复杂性提高，而均以增加规模的成本。这些增长反过来又使研究人员更难迅速原型新想法或复制已发表的RL算法。为了解决这些问题，这项工作描述了ACME，这是一个用于构建新型RL算法的框架，这些框架是专门设计的，用于启用使用简单的模块化组件构建的代理，这些组件可以在各种执行范围内使用。尽管ACME的主要目标是为算法开发提供一个框架，但第二个目标是提供重要或最先进算法的简单参考实现。这些实现既是对我们的设计决策的验证，也是对RL研究中可重复性的重要贡献。在这项工作中，我们描述了ACME内部做出的主要设计决策，并提供了有关如何使用其组件来实施各种算法的进一步详细信息。我们的实验为许多常见和最先进的算法提供了基准，并显示了如何为更大且更复杂的环境扩展这些算法。这突出了ACME的主要优点之一，即它可用于实现大型，分布式的RL算法，这些算法可以以较大的尺度运行，同时仍保持该实现的固有可读性。这项工作提出了第二篇文章的版本，恰好与模块化的增加相吻合，对离线，模仿和从演示算法学习以及作为ACME的一部分实现的各种新代理。

Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-theart linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5× less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers.