卷积神经网络已使基于医学图像的诊断有了重大改进。但是，越来越明显的是，这些模型在面对虚假的相关性和数据集转移时易受性能降解，例如，领导者（例如，代表性不足的患者群体的表现不足）。在本文中，我们比较了ADNI MRI数据集上的两个分类方案：使用手动选择的体积特征的简单逻辑回归模型，以及对3D MRI数据训练的卷积神经网络。我们在面对不同的数据集拆分，训练集的性别组成和疾病阶段的情况下评估了受过训练的模型的鲁棒性。与其他成像方式中的早期工作相反，我们没有观察到培训数据集中多数组的模型性能的明确模式。取而代之的是，尽管逻辑回归对数据集组成完全可靠，但我们发现，在培训数据集中包括更多女性受试者时，男性和女性受试者的CNN性能通常会提高。我们假设这可能是由于两性病理学的固有差异。此外，在我们的分析中，Logistic回归模型优于3D CNN，强调了基于先验知识的手动特征规范的实用性，以及需要更强大的自动功能选择。

预计机器学习将推动医疗保健的重大改善。为了确保尊重诸如福利，尊重人类自治，预防伤害，正义，隐私和透明度之类的基本原则，必须负责任地发展医疗机器学习系统。为此，已经提出了许多道德原则的高级声明，但是严重缺乏技术准则，阐明了医疗机器学习的实际后果。同样，目前，关于医疗机器学习系统的确切监管要求也存在很大的不确定性。这项调查概述了以负责任地创建医疗机器学习系统并符合现有法规以及解决这些挑战的可能解决方案所涉及的技术和程序挑战。首先，对影响医学机器学习的现有法规进行了简要审查，表明诸如安全性，鲁棒性，可靠性，隐私，安全性，透明度，解释性和非歧视性诸如现有法律和法规已经要求，尽管在许多情况下，但许多人都要求案例，不确定。接下来，讨论了实现这些理想特性的关键技术障碍，以及在医学背景下克服这些障碍的重要技术。我们注意到分配变化，虚假相关性，模型未指定，不确定性量化和数据稀缺性代表了医疗环境中的严重挑战。有希望的解决方案方法包括使用大型和代表性的数据集和联合学习作为目的的手段，仔细的域知识的利用，固有透明的模型的使用，全面的分布模型测试和验证以及算法影响评估。

We study the generalization capacity of group convolutional neural networks. We identify precise estimates for the VC dimensions of simple sets of group convolutional neural networks. In particular, we find that for infinite groups and appropriately chosen convolutional kernels, already two-parameter families of convolutional neural networks have an infinite VC dimension, despite being invariant to the action of an infinite group.

Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by (1) reducing transmission via identifying cases, and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest - one's positive infectious status, is often a latent variable. In addition, presence of both network and temporal dependence reduces the data to a single observation. As testing entire populations regularly is neither efficient nor feasible, standard approaches to testing recommend simple rule-based testing strategies (e.g., symptom based, contact tracing), without taking into account individual risk. In this work, we study an adaptive sequential design involving n individuals over a period of {\tau} time-steps, which allows for unspecified dependence among individuals and across time. Our causal target parameter is the mean latent outcome we would have obtained after one time-step, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. We propose an Online Super Learner for adaptive sequential surveillance that learns the optimal choice of tests strategies over time while adapting to the current state of the outbreak. Relying on a series of working models, the proposed method learns across samples, through time, or both: based on the underlying (unknown) structure in the data. We present an identification result for the latent outcome in terms of the observed data, and demonstrate the superior performance of the proposed strategy in a simulation modeling a residential university environment during the COVID-19 pandemic.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants$\unicode{x2014}$what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world$\unicode{x2014}$also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing$\unicode{x2014}$leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.

Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need for higher or lower resolution, which varies over space and time. We present a novel formulation of AMR as a fully-cooperative Markov game, in which each element is an independent agent who makes refinement and de-refinement choices based on local information. We design a novel deep multi-agent reinforcement learning (MARL) algorithm called Value Decomposition Graph Network (VDGN), which solves the two core challenges that AMR poses for MARL: posthumous credit assignment due to agent creation and deletion, and unstructured observations due to the diversity of mesh geometries. For the first time, we show that MARL enables anticipatory refinement of regions that will encounter complex features at future times, thereby unlocking entirely new regions of the error-cost objective landscape that are inaccessible by traditional methods based on local error estimators. Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics. We show that learned policies generalize to test problems with physical features, mesh geometries, and longer simulation times that were not seen in training. We also extend VDGN with multi-objective optimization capabilities to find the Pareto front of the tradeoff between cost and error.

最近，与神经网络的时间相关微分方程的解决方案最近引起了很多关注。核心思想是学习控制解决方案从数据演变的法律，该数据可能会被随机噪声污染。但是，与其他机器学习应用相比，通常对手头的系统了解很多。例如，对于许多动态系统，诸如能量或（角度）动量之类的物理量是完全保守的。因此，神经网络必须从数据中学习这些保护定律，并且仅由于有限的训练时间和随机噪声而被满足。在本文中，我们提出了一种替代方法，该方法使用Noether的定理将保护定律本质地纳入神经网络的体系结构。我们证明，这可以更好地预测三个模型系统：在三维牛顿引力潜能中非偏见粒子的运动，Schwarzschild指标中庞大的相对论粒子的运动和两个相互作用的粒子在四个相互作用的粒子系统中的运动方面。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

使用深度学习技术，可以在MRI图像中自动检测到旁那鼻鼻窦系统中的异常，并可以根据其体积，形状和其他参数（例如局部对比度）进行进一步分析和分类。但是，由于培训数据有限，传统的监督学习方法通​​常无法概括。现有的旁那间异常分类中的深度学习方法最多可诊断出一种异常。在我们的工作中，我们考虑三个异常。具体而言，我们采用3D CNN来分离上颌鼻窦体积，而没有异常的鼻窦体积，并具有异常。为了从一个小标记的数据集中学习强大的表示形式，我们提出了一种新颖的学习范式，结合了对比损失和跨内向损失。特别是，我们使用有监督的对比损失，鼓励有或没有异常的上颌窦量的嵌入来形成两个不同的簇，而跨层损失则鼓励3D CNN保持其歧视能力。我们报告说，两种损失的优化是有利的，而不是仅通过一次损失而优化。我们还发现我们的培训策略会提高标签效率。使用我们的方法，3D CNN分类器的AUROC为0.85，而用横向渗透损失优化的3D CNN分类器可实现0.66的AUROC。

经典算法和机器学习系统（如神经网络）在日常生活中都很丰富。虽然经典的计算机科学算法适合精确执行精确定义的任务，例如在大图中找到最短路径，但神经网络允许从数据中学习来预测更为复杂的任务中最可能的答案，例如图像分类，无法减少。到确切的算法。为了获得两全其美，本文探讨了将这两个概念结合起来，从而导致更健壮，表现更好，更容易解释，计算效率更高，并且具有更高的数据有效体系结构。该论文正式化了算法监督的想法，该算法可以使神经网络与算法一起学习或结合学习。当将算法集成到神经体系结构中时，重要的是，算法是可区分的，因此可以端对端训练架构，并且可以以有意义的方式通过算法传播梯度。为了使算法可区分，本文提出了一种通过扰动变量并以封闭形式的期望值（即无需采样）近似期望值来连续放松算法的通用方法。此外，本文提出了可区分的算法，例如可区分的排序网络，可区分的渲染器和可区分的逻辑门网络。最后，本文提出了使用算法学习的替代培训策略。