标准化流是可易处理的密度模型，可以近似复杂的目标分布，例如物理系统的玻尔兹曼分布。但是，当前的训练流量要么具有寻求模式的行为，要么使用昂贵的MCMC模拟事先生成的目标样本，要么使用具有很高差异的随机损失。为了避免这些问题，我们以退火重要性采样（AIS）增强流量，并最大程度地减少覆盖$ \ alpha $ -divergence的质量，并使用$ \ alpha = 2 $，从而最大程度地减少了重要性的重量差异。我们的方法是流动性Bootstrap（Fab），使用AIS在流动较差的目标区域中生成样品，从而促进了新模式的发现。我们以AIS的最小差异分布来定位，以通过重要性抽样来估计$ \ alpha $ -Divergence。我们还使用优先的缓冲区来存储和重复使用AIS样本。这两个功能显着提高了Fab的性能。我们将FAB应用于复杂的多模式目标，并表明我们可以在以前的方法失败的情况下非常准确地近似它们。据我们所知，我们是第一个仅使用非均衡目标密度学习丙氨酸二肽分子的玻璃体分布，而无需通过分子动力学（MD）模拟生成的样品：FAB与通过最大可能性训练更好的效果，而不是通过最大可能性产生的结果。在MD样品上使用100倍的目标评估。在重新获得重要权重的样品后，我们获得了与地面真相几乎相同的二面角的无偏直方图。

归一化流量是灵活的，参数化分布，可用于通过重要性采样从难治性分布中的预期近似。然而，目前的基于流动的方法受到挑战目标的限制，其中它们患有模式寻求行为或在训练损失中的高方差，或依赖于目标分布的样本，这可能不可用。为了解决这些挑战，我们将流量与退火重点采样（AIS）相结合，同时使用$ \ Alpha $ - 在新颖的培训程序中使用$ \ Alpha $ - 作为我们的目标，在培训程序Fab（Flow AIS Bootstrap）中。因此，流动和AI以自动启动方式彼此改进。我们展示了FAB可以用于对复杂的目标分布产生准确的近似，包括Boltzmann分布，在前一种基于流基的方法失败的问题中。

This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its individual components by utilising distillation. While doing so it tries to maximise the profit produced by the distillation sequence. All actions of the agent were set by the SAC agent in Python and communicated in Aspen Plus via an API. Here the distillation column was simulated by use of the build-in RADFRAC column. With this a connection was established for data transfer between Python and Aspen and the agent succeeded to show learning behaviour, while increasing profit. Although results were generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable for parallelisation. This makes that Aspen is incompatible for solving RL problems. Code and thesis are available at https://github.com/lollcat/Aspen-RL

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

Because noise can interfere with downstream analysis, image denoising has come to occupy an important place in the image processing toolbox. The most accurate state-of-the-art denoisers typically train on a representative dataset. But gathering a training set is not always feasible, so interest has grown in blind zero-shot denoisers that train only on the image they are denoising. The most accurate blind-zero shot methods are blind-spot networks, which mask pixels and attempt to infer them from their surroundings. Other methods exist where all neurons participate in forward inference, however they are not as accurate and are susceptible to overfitting. Here we present a hybrid approach. We first introduce a semi blind-spot network where the network can see only a small percentage of inputs during gradient update. We then resolve overfitting by introducing a validation scheme where we split pixels into two groups and fill in pixel gaps using domino tilings. Our method achieves an average PSNR increase of $0.28$ and a three fold increase in speed over the current gold standard blind zero-shot denoiser Self2Self on synthetic Gaussian noise. We demonstrate the broader applicability of Pixel Domino Tiling by inserting it into a preciously published method.

Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic samples of high-resolution rainfall based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.

Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.

从不同的随机初始化开始，经过随机梯度下降（SGD）训练的神经网络通常在功能上非常相似，从而提出了一个问题，即不同的SGD溶液之间是否存在有意义的差异。 Entezari等。最近猜想，尽管初始化不同，但在考虑到神经网络的置换不变性后，SGD发现的解决方案位于相同的损失谷中。具体而言，他们假设可以将SGD找到的任何两种解决方案排列，以使其参数之间的线性插值形成一条路径，而不会显着增加损失。在这里，我们使用一种简单但功能强大的算法来找到这样的排列，使我们能够获得直接的经验证据，证明该假设在完全连接的网络中是正确的。引人注目的是，我们发现在初始化时已经存在两个网络，并且平均它们随机，但适当排列的初始化的性能大大高于机会。相反，对于卷积架构，我们的证据表明该假设不存在。特别是在大型学习率制度中，SGD似乎发现了各种模式。

多标签分类器估计每一组概念标签的二进制标签状态（相关与无关），对于任何给定的实例。概率多标签分类器在此类标签状态（标签的幂列）的所有可能的标签组组合（标签的功能）的所有可能的标签集组合中提供了预测性的后验分布，我们可以通过选择对应于该分布的最大预期准确性的标签集，从而提供最佳的估计值。例如，在最大化精确匹配精度时，我们提供了分布的模式。但是，这与我们在这样的估计中可能拥有的信心有何关系？置信度是多标签分类器（通常在机器学习中）现实世界应用的重要组成部分，并且是解释性和解释性的重要组成部分。但是，如何在多标签上下文中提供信心并与特定准确度量有关，也不清楚如何提供与预期准确性良好相关的信心，这在现实中最有价值 - 世界决策。在本文中，我们将预期准确性视为具有给定精度度量的信心的替代品。我们假设可以从多标签预测分布中估算预期精度。我们检查了七个候选功能，以估计预测分布的预期准确性的能力。我们发现其中三个与预期准确性相关，并且具有稳健性。此外，我们确定可以单独使用每个候选功能来估计锤击相似性，但是候选者的组合最适合预期的jaccard索引和精确匹配。

机器学习和特别是强化学习（RL）在帮助我们了解神经决策过程方面非常成功。但是，RL在理解其他神经过程中的作用，尤其是运动学习的探索程度要少得多。为了探索这种联系，我们研究了最近的深度RL方法与基于错误的学习神经科学中的主要运动学习框架相对应。可以使用镜面反转适应范式探测基于错误的学习，在该范式中，它产生了独特的定性预测，这些预测在人类中观察到。因此，我们在镜面逆向上测试了现代深度RL算法的三个主要家庭。令人惊讶的是，所有算法都无法模仿人类的行为，并且确实表现出与基于错误的学习预测的行为。为了填补这一空白，我们引入了一种新颖的深度RL算法：基于模型的确定性策略梯度（MB-DPG）。 MB-DPG通过明确依靠观察到的动作结果来从基于错误的学习中汲取灵感。我们在镜像和旋转扰动下显示MB-DPG捕获（人）基于错误的学习。接下来，我们以MB-DPG的形式展示了基于错误的学习，比基于复杂的ARM的到达任务的规范无模型算法更快，同时比基于模型的RL更适合（正向）模型错误。这些发现突出了当前的深度RL方法与人类电动机适应之间的差距，并提供了缩小这一差距的途径，从而促进了两个领域之间未来的有益相互作用。