深度强化学习（或仅仅是“ RL”）在工业和研究应用中广受欢迎。但是，它仍然受到一些关键限制，从而减慢了广泛的采用。它的性能对初始条件和非确定性敏感。为了释放这些挑战，我们提出了一种建立RL代理合奏的程序，以有效地建立更好的本地决策，以实现长期累积的回报。首次进行了数百个实验，以比较2个电力控制环境中的不同集合构造程序。我们发现，由4个代理商组成的合奏提高了46％的累积奖励，将重现性提高了3.6，并且可以自然有效地训练和预测GPU和CPU。

相机陷阱彻底改变了许多物种的动物研究，这些物种以前由于其栖息地或行为而几乎无法观察到。它们通常是固定在触发时拍摄短序列图像的树上的相机。深度学习有可能克服工作量以根据分类单元或空图像自动化图像分类。但是，标准的深神经网络分类器失败，因为动物通常代表了高清图像的一小部分。这就是为什么我们提出一个名为“弱对象检测”的工作流程，以更快的速度rcnn+fpn适合这一挑战。该模型受到弱监督，因为它仅需要每个图像的动物分类量标签，但不需要任何手动边界框注释。首先，它会使用来自多个帧的运动自动执行弱监督的边界框注释。然后，它使用此薄弱的监督训练更快的RCNN+FPN模型。来自巴布亚新几内亚和密苏里州生物多样性监测活动的两个数据集获得了实验结果，然后在易于重复的测试台上获得了实验结果。

深度神经网络（DNN）的集合已经实现了定性预测，但它们是计算和记忆密集型的。因此，需求越来越多，以使他们通过可用的计算资源来回答大量的请求。与最近针对单个DNN的预测推理服务器和推理框架的计划不同，我们提出了一个新的软件层，以灵活性和效率DNNS的合奏服务。我们的推理系统设计了几项技术创新。首先，我们提出了一个新的程序，以在设备（CPU或GPU）和DNN实例之间找到良好的分配矩阵。它连续运行最差的功能，可以将DNN分配到存储器设备和贪婪的算法中，以优化分配设置并加快合奏。其次，我们根据多个过程设计推理系统，以异步运行：批处理，预测和结合规则，具有有效的内部通信方案，以避免开销。实验显示了极端情况下的灵活性和效率：成功地将12个重型DNN的合奏提供到4 GPU中，而在相反的相反，一个单个DNN多线程为16 GPU。它还胜过简单的基线，该基线包括在图像分类任务上通过高达2.7倍的加速度优化DNN的批处理大小。

结合（或带有结合）的自动化机器学习试图自动构建深度神经网络（DNNS）的合奏，以实现定性的预测。众所周知，DNN的合奏避免过度合身，但它们是记忆和耗时的方法。因此，理想的汽车将在一次运行时间内产生有关准确性和推理速度的不同集合。尽管以前的AutoML专注于搜索最佳模型以最大化其概括能力，但我们宁愿提出新的Automl来构建一个较大的精确和多样化的单个模型的库，以构建合奏。首先，我们的广泛基准显示异步超频带是一种有效且可靠的方法，可以构建大量不同的模型来组合它们。然后，提出了一种基于多目标贪婪算法的新合奏选择方法，以通过控制其计算成本来生成准确的合奏。最后，我们提出了一种新型算法，以根据分配优化优化GPU群集中DNNS集合的推断。使用集合方法产生的自动素体在训练阶段和推理阶段都使用有效的GPU簇在两个数据集上显示出强大的结果。

Neural style transfer is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image and is particularly impressive when it comes to transferring style from a painting to an image. It was originally achieved by solving an optimization problem to match the global style statistics of the style image while preserving the local geometric features of the content image. The two main drawbacks of this original approach is that it is computationally expensive and that the resolution of the output images is limited by high GPU memory requirements. Many solutions have been proposed to both accelerate neural style transfer and increase its resolution, but they all compromise the quality of the produced images. Indeed, transferring the style of a painting is a complex task involving features at different scales, from the color palette and compositional style to the fine brushstrokes and texture of the canvas. This paper provides a solution to solve the original global optimization for ultra-high resolution images, enabling multiscale style transfer at unprecedented image sizes. This is achieved by spatially localizing the computation of each forward and backward passes through the VGG network. Extensive qualitative and quantitative comparisons show that our method produces a style transfer of unmatched quality for such high resolution painting styles.

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.

State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also known as skull-stripping) and manual correction by an expert. However, for clinical practice, this last step is tedious and time-consuming and, therefore, not always feasible, resulting in skull-stripping faults that can negatively impact the tumor segmentation quality. Still, the extent of this impact has never been measured for any of the many different BE methods available. In this work, we propose an automatic brain tumor segmentation pipeline and evaluate its performance with multiple BE methods. Our experiments show that the choice of a BE method can compromise up to 15.7% of the tumor segmentation performance. Moreover, we propose training and testing tumor segmentation models on non-skull-stripped images, effectively discarding the BE step from the pipeline. Our results show that this approach leads to a competitive performance at a fraction of the time. We conclude that, in contrast to the current paradigm, training tumor segmentation models on non-skull-stripped images can be the best option when high performance in clinical practice is desired.

Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of the system need to have more information and reliability for the actions executed by an agent. In this regard, explainable reinforcement learning seeks to provide to an agent in training with methods in order to explain its behavior in such a way that users with no experience in machine learning could understand the agent's behavior. One of these is the memory-based explainable reinforcement learning method that is used to compute probabilities of success for each state-action pair using an episodic memory. In this work, we propose to make use of the memory-based explainable reinforcement learning method in a hierarchical environment composed of sub-tasks that need to be first addressed to solve a more complex task. The end goal is to verify if it is possible to provide to the agent the ability to explain its actions in the global task as well as in the sub-tasks. The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.

A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean \textit{k}-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.

Natural language inference has trended toward studying contexts beyond the sentence level. An important application area is law: past cases often do not foretell how they apply to new situations and implications must be inferred. This paper introduces LawngNLI, constructed from U.S. legal opinions with automatic labels with high human-validated accuracy. Premises are long and multigranular. Experiments show two use cases. First, LawngNLI can benchmark for in-domain generalization from short to long contexts. It has remained unclear if large-scale long-premise NLI datasets actually need to be constructed: near-top performance on long premises could be achievable by fine-tuning using short premises. Without multigranularity, benchmarks cannot distinguish lack of fine-tuning on long premises versus domain shift between short and long datasets. In contrast, our long and short premises share the same examples and domain. Models fine-tuned using several past NLI datasets and/or our short premises fall short of top performance on our long premises. So for at least certain domains (such as ours), large-scale long-premise datasets are needed. Second, LawngNLI can benchmark for implication-based retrieval. Queries are entailed or contradicted by target documents, allowing users to move between arguments and evidence. Leading retrieval models perform reasonably zero shot on a LawngNLI-derived retrieval task. We compare different systems for re-ranking, including lexical overlap and cross-encoders fine-tuned using a modified LawngNLI or past NLI datasets. LawngNLI can train and test systems for implication-based case retrieval and argumentation.