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.

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。

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。

我们开发了ShiftMatch，这是贝叶斯神经网络（BNNS）中出现的新型训练数据依赖性的可能性（OOD）鲁棒性。ShiftMatch的灵感来自Izmailov等人的训练数据依赖性“ Empcov”先验。（2021a）并有效地与训练时的测试时间空间相关性匹配。至关重要的是，ShiftMatch旨在使神经网络训练保持不变，从而使其可以使用预定的BNN公开样品。使用预训练的HMC样品，ShiftMatch在CIFAR-10-C上具有强大的性能提高，效果优于Empcov先验，并且也许是第一种能够令人信服地超过普通的深层合奏的贝叶斯方法。可以将ShiftMatch与非乘式合奏等非乘坐方法集成，其中提供了较小但仍然相当大的性能改进。总体而言，贝叶斯的ShiftMatch具有比ShiftMatch的合奏的精度稍好一些，尽管它们都具有非常相似的对数。

现代深度神经网络（DNN）的成功基于其在多层转换投入以建立良好高级表示的能力。因此，了解这种表示学习过程至关重要。但是，我们不能使用涉及无限宽度限制的标准理论方法，因为它们消除了代表性学习。因此，我们开发了一个新的无限宽度限制，即表示的学习限制，该限制表现出表示形式的学习反映，但在有限宽度网络中，但同时仍然非常容易处理。例如，表示学习限制在深处的高斯过程中提供了恰好具有多种内核的多元高斯后期，包括所有各向同性（距离依赖）内核。我们得出一个优雅的目标，描述了每个网络层如何学习在输入和输出之间插值的表示形式。最后，我们使用此限制和目标来开发对内核方法的灵活，深刻的概括，我们称之为深内核机器（DKMS）。我们表明，可以使用受高斯过程文献中诱导点方法启发的方法将DKMS缩放到大数据集，并且我们表明DKMS表现出优于其他基于内核方法的性能。

最近的工作引入了深度内核过程作为NNS的完全基于内核的替代方案（Aitchison等人.2020）。深入的内核过程通过在正半定矩阵上的分布和执行非线性变换的分布中来灵活地学习良好的顶层表示。一个特定的深核流程，深度愿望过程（DWP）是特别令人感兴趣的，因为它可以与其相当于可以完全表达克矩阵的核的深层高斯过程（DGP）前沿。然而，由于缺乏积极半定矩阵的分布缺乏足够灵活的分布，DWP的推断尚未实现。在这里，我们通过推广Bartlett对概率密度的分解来提供一种新的半定矩阵在正半定矩阵上获得灵活分布的新方法。我们使用这个新的分发来开发包括跨层依赖的DWP的近似后验。我们为DWP开发了双随机诱导点推理方案，实验显示DWP中的推断可以提高在DGP中的性能，在DGP中具有相同的产品。

包含数据增强的贝叶斯神经网络隐含地使用“无随机扰动的日志 - 似然，[哪个]没有作为有效的似然函数的干净解释''（Izmailov等，2021）。在这里，我们为开发具有数据增强的原则贝叶斯神经网络的方法提供了几种方法。我们介绍了一个“有限轨道”的设置，允许完全计算似然性，并在更常见的“完全轨道”设置中为更紧密的多样本限制。这些模型在寒冷后效应的起源上投射光线。特别是，我们发现甚至在包括数据增强的这些原则模型中仍然存在寒冷的后效。这表明冷的后效不能使用不正确的可能性作为数据增强的伪影。

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.

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.