尽管预测方法的相关性越来越高，但这些算法的因果影响仍然很大程度上是未开发的。这与考虑到，即使在简化因果充足之类的假设下，模型的统计风险也可能与其\ Textit {因果风险}有显着差异。在这里，我们研究了*因果概括* - 从观察到介入分布的概括 - 预测。我们的目标是找到问题的答案：自回归（var）模型在预测统计协会方面的疗效如何与其在干预措施下预测的能力相比？为此，我们介绍了*因果学习理论*预测的框架。使用此框架，我们获得了统计和因果风险之间差异的表征，这有助于识别它们之间的分歧源。在因果充足之下，因果概括的因果概括金额与额外的结构（限制介入介入分配）。该结构允许我们获得统一的收敛界面对VAR模型类的因果概括性。据我们所知，这是第一个为时序设置中因果概念提供理论保障的工作。

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Biometrics are one of the most privacy-sensitive data. Ubiquitous authentication systems with a focus on privacy favor decentralized approaches as they reduce potential attack vectors, both on a technical and organizational level. The gold standard is to let the user be in control of where their own data is stored, which consequently leads to a high variety of devices used. Moreover, in comparison with a centralized system, designs with higher end-user freedom often incur additional network overhead. Therefore, when using face recognition for biometric authentication, an efficient way to compare faces is important in practical deployments, because it reduces both network and hardware requirements that are essential to encourage device diversity. This paper proposes an efficient way to aggregate embeddings used for face recognition based on an extensive analysis on different datasets and the use of different aggregation strategies. As part of this analysis, a new dataset has been collected, which is available for research purposes. Our proposed method supports the construction of massively scalable, decentralized face recognition systems with a focus on both privacy and long-term usability.

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.

使用高斯混合模型（GMM）的变异推断能够学习可侵入性目标分布的高度扣除但多模式的近似值。 GMM与最多数百个维度的问题设置特别相关，例如机器人技术，用于对轨迹或联合分布进行建模。这项工作着重于基于GMM的两种非常有效的方法，这些方法既采用独立的自然梯度更新来为单个组件和权重的分类分布。我们首次表明，尽管它们的实际实现和理论保证有所不同，但他们的派生更新是等效的。我们确定了几种设计选择，可以区分两种方法，即在样本选择，自然梯度估计，步骤适应以及信任区域是否得到强制或适应的组件数量方面。我们对这些设计选择进行广泛的消融，并表明它们强烈影响了优化的效率和学习分布的可变性。基于我们的见解，我们提出了对广义框架的新颖实例化，该实例将一阶自然梯度估计与信任区域和组件适应相结合，并且在我们所有实验中都显着优于以前的两种方法。

脑转移经常发生在转移性癌症的患者中。早期和准确地检测脑转移对于放射治疗的治疗计划和预后至关重要。为了提高深入学习的脑转移检测性能，提出了一种称为体积级灵敏度特异性（VSS）的定制检测损失，该损失是单个转移检测灵敏度和（子）体积水平的特异性。作为敏感性和精度始终在转移水平中始终是折射率，可以通过调节VSS损耗中的重量而无需骰子分数系数进行分段转移来实现高精度或高精度。为了减少被检测为假阳性转移的转移样结构，提出了一种时间的现有量作为神经网络的额外输入。我们提出的VSS损失提高了脑转移检测的敏感性，将灵敏度提高了86.7％至95.5％。或者，它将精度提高了68.8％至97.8％。随着额外的时间现有量，在高灵敏度模型中，约45％的假阳性转移减少，高特异性模型的精度达到99.6％。所有转移的平均骰子系数约为0.81。随着高灵敏度和高特异性模型的集合，平均每位患者的1.5个假阳性转移需要进一步检查，而大多数真正的阳性转移确认。该集合学习能够区分从需要特殊专家审查或进一步跟进的转移候选人的高信心真正的阳性转移，特别适合实际临床实践中专家支持的要求。

Schr \“Odinger方程的准确数字解决方案在量子化学方面至关重要。然而，当前高精度方法的计算成本与交互粒子的数量相当差。最近将Monte Carlo方法与无监督的神经网络训练相结合被提议作为克服该环境中的维度诅咒的有希望的方法，并以适度缩放的计算成本获得各个分子的准确的波力。这些方法目前不会利用波力源相对于它们的分子几何形状表现出的规律性。灵感来自最近的近期转移学习在机器翻译和计算机视觉任务中的成功应用，我们试图通过在优化基于神经网络的模型以进行不同分子几何形状时引入权重共享限制来利用这种规律。也就是说，我们限制了优化过程高达95％的w神经网络模型中的八个实际上是相同的分类几何形状。我们发现，当通过数量级考虑相同分子的核几何形状时，该技术可以加速优化，并且它开启了朝向预训练的神经网络波力发射的有希望的路线，即使在不同的分子上也能产生高精度。

我们提出了一个新的因果贡献的概念，它描述了在DAG中目标节点上的节点的“内在”部分。我们显示，在某些情况下，现有的因果量化方法无法完全捕获此概念。通过以上游噪声术语递归地将每个节点写入每个节点，我们将每个节点添加的内部信息分开从其祖先所获得的每个节点添加的内部信息。要将内在信息解释为因果贡献，我们考虑“结构保留干预”，该介绍每个节点随机化，以一种模仿通常依赖父母的方式，也不会扰乱观察到的联合分布。为了获得跨越节点的任意排序的措施，我们提出了基于福利的对称化。我们描述了对方差和熵的贡献分析，但可以类似地定义对其他目标度量的贡献。

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate the benefits of this principle through RLlib: a library that provides scalable software primitives for RL. These primitives enable a broad range of algorithms to be implemented with high performance, scalability, and substantial code reuse. RLlib is available as part of the open source Ray project 1 .

Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks. The two main challenges are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data. We address the first challenge by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias, with an exponentially-weighted estimator of the advantage function that is analogous to TD(λ). We address the second challenge by using trust region optimization procedure for both the policy and the value function, which are represented by neural networks. Our approach yields strong empirical results on highly challenging 3D locomotion tasks, learning running gaits for bipedal and quadrupedal simulated robots, and learning a policy for getting the biped to stand up from starting out lying on the ground. In contrast to a body of prior work that uses hand-crafted policy representations, our neural network policies map directly from raw kinematics to joint torques. Our algorithm is fully model-free, and the amount of simulated experience required for the learning tasks on 3D bipeds corresponds to 1-2 weeks of real time.