Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.Recently, large scale quantum chemistry calculation and molecular dynamics simulations coupled with advances in high throughput experiments have begun to generate data at an unprecedented rate. Most classical techniques do not make effective use of the larger amounts of data that are now available. The time is ripe to apply more powerful and flexible machine learning methods to these problems, assuming we can find models with suitable inductive biases. The symmetries of atomic systems suggest neural networks that operate on graph structured data and are invariant to graph isomorphism might also be appropriate for molecules. Sufficiently successful models could someday help automate challenging chemical search problems in drug discovery or materials science.In this paper, our goal is to demonstrate effective machine learning models for chemical prediction problems

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.

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat their illness. Making decisions using just the expected future returns -- known in reinforcement learning as the value -- cannot account for the potential range of adverse or positive outcomes a decision may have. Therefore, we should use the distribution over expected future returns differently to represent the critical information that the agent requires at decision time by taking both the future and accrued returns into consideration. In this paper, we propose two novel Monte Carlo tree search algorithms. Firstly, we present a Monte Carlo tree search algorithm that can compute policies for nonlinear utility functions (NLU-MCTS) by optimising the utility of the different possible returns attainable from individual policy executions, resulting in good policies for both risk-aware and multi-objective settings. Secondly, we propose a distributional Monte Carlo tree search algorithm (DMCTS) which extends NLU-MCTS. DMCTS computes an approximate posterior distribution over the utility of the returns, and utilises Thompson sampling during planning to compute policies in risk-aware and multi-objective settings. Both algorithms outperform the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns.

许多现实世界中的问题都包含多个目标和代理，其中目标之间存在权衡。解决此类问题的关键是利用代理之间存在的稀疏依赖性结构。例如，在风电场控制中，在最大化功率和最大程度地减少对系统组件的压力之间存在权衡。涡轮机之间的依赖性是由于唤醒效应而产生的。我们将这种稀疏依赖性模拟为多目标配位图（MO-COG）。在多目标强化学习实用程序功能通常用于对用户偏好而不是目标建模，这可能是未知的。在这种情况下，必须计算一组最佳策略。哪些策略是最佳的，取决于哪些最佳标准适用。如果用户的效用函数是从策略的多个执行中得出的，则必须优化标识的预期收益（SER）。如果用户的效用是从策略的单个执行中得出的，则必须优化预期的标量回报（ESR）标准。例如，风电场受到必须始终遵守的限制和法规，因此必须优化ESR标准。对于Mo-COG，最新的算法只能计算一组SER标准的最佳策略，而ESR标准进行了研究。要计算在ESR标准下（也称为ESR集合）下的一组最佳策略，必须维护回报上的分布。因此，为了计算MO-COGS的ESR标准下的一组最佳策略，我们提出了一种新型的分布多目标变量消除（DMOVE）算法。我们在逼真的风电场模拟中评估了DMOVE。鉴于实际风电场设置中的回报是连续的，我们使用称为Real-NVP的模型来学习连续的返回分布来计算ESR集合。

自动生物医学图像分析的领域至关重要地取决于算法验证的可靠和有意义的性能指标。但是，当前的度量使用通常是不明智的，并且不能反映基本的域名。在这里，我们提出了一个全面的框架，该框架指导研究人员以问题意识的方式选择绩效指标。具体而言，我们专注于生物医学图像分析问题，这些问题可以解释为图像，对象或像素级别的分类任务。该框架首先编译域兴趣 - 目标结构 - ，数据集和算法与输出问题相关的属性的属性与问题指纹相关，同时还将其映射到适当的问题类别，即图像级分类，语义分段，实例，实例细分或对象检测。然后，它指导用户选择和应用一组适当的验证指标的过程，同时使他们意识到与个人选择相关的潜在陷阱。在本文中，我们描述了指标重新加载推荐框架的当前状态，目的是从图像分析社区获得建设性的反馈。当前版本是在由60多个图像分析专家的国际联盟中开发的，将在社区驱动的优化之后公开作为用户友好的工具包提供。

Modern machine learning models are opaque, and as a result there is a burgeoning academic subfield on methods that explain these models' behavior. However, what is the precise goal of providing such explanations, and how can we demonstrate that explanations achieve this goal? Some research argues that explanations should help teach a student (either human or machine) to simulate the model being explained, and that the quality of explanations can be measured by the simulation accuracy of students on unexplained examples. In this work, leveraging meta-learning techniques, we extend this idea to improve the quality of the explanations themselves, specifically by optimizing explanations such that student models more effectively learn to simulate the original model. We train models on three natural language processing and computer vision tasks, and find that students trained with explanations extracted with our framework are able to simulate the teacher significantly more effectively than ones produced with previous methods. Through human annotations and a user study, we further find that these learned explanations more closely align with how humans would explain the required decisions in these tasks. Our code is available at https://github.com/coderpat/learning-scaffold

对于科学家来说，准确的密度功能的系统开发一直是数十年来的挑战。尽管机器学习（ML）在近似功能中的新兴应用，但所得的ML功能通常包含数十万个参数，这与常规的人类设计的符号符号函数构成了巨大的差距。我们提出了一个新的框架，符号功能进化搜索（SYFES），该搜索会自动以符号形式构造准确的功能，该功能比人类更便宜，并且比其他ML功能更易于评估，并且更易于整合到现有的密度功能理论代码。我们首先表明，没有先验知识，Syfes从头开始重建了已知的功能。然后，我们证明，从现有的功能性$ \ omega $ b9.7亿v演变，Syfes发现了一种新的功能性GAS22（Google Accelated Science 22），在主要组化学数据库的测试集中，大多数分子类型的表现更好（ MGCDB84）。我们的框架为利用计算能力的新方向开发了符号密度函数的系统开发。

框架转移是翻译中的横向现象，导致相应的语言材料对唤起不同帧。预测帧移位的能力使通过注释投影自动创建多语言架构。这里，我们提出了帧移位预测任务，并演示了图表关注网络，与辅助训练相结合，可以学习跨语言帧到帧对应关系并预测帧移位。

在许多实际情况下，用户的实用程序来自策略的单个执行。在这种情况下，要应用多目标增强学习，必须优化收益的预期效用。存在各种方案，其中用户对目标（也称为实用程序功能）的偏好是未知或难以指定的。在这种情况下，必须学习一组最佳政策。但是，多目标增强学习社区必须最大程度地忽略了必须最大程度地提高预期效用的设置，结果，一组最佳解决方案尚未定义。在本文中，我们通过提出一阶随机优势作为建立解决方案集以最大化预期效用的标准来应对这一挑战。我们还提出了一种新的优势标准，称为预期标量回报（ESR）优势，该标准率扩展了一阶随机优势，以允许在实践中学习一组最佳策略。然后，我们定义一个称为ESR集的新解决方案概念，该概念是ESR主导的一组策略。最后，我们定义了一种新的多目标分布表格增强学习（MOT-DRL）算法，以在多目标多臂强盗设置中学习设置的ESR。

逻辑回归是一种用于二进制分类的常用方法。研究人员通常具有多个二进制响应变量，并且同时分析是有益的，因为它可以深入了解响应变量之间的依赖性以及预测变量和响应之间的依赖性。此外，在这样的同时分析中，方程式可以相互借出强度，这可能会提高预测精度。在本文中，我们提出了同时二进制逻辑回归建模的旋律家族。在这个家族中，基于距离规则，在降低维度的欧几里得空间中定义了回归模型。该模型可以用逻辑回归系数或双皮子来解释。我们讨论了用于参数估计的快速迭代术（或MM）算法。详细显示了两种应用：一种将人格特征与药物消费概况有关的应用，以及一个与抑郁症和焦虑症有关的人格特征。我们将旋律家族与多元二元数据的替代方法进行了详尽的比较。