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.

我们提出了一种新颖的机器学习体系结构，双光谱神经网络（BNNS），用于学习数据的数据表示，这些数据是对定义信号的空间中组的行为不变的。该模型结合了双光谱的ANSATZ，这是一个完整的分析定义的组不变的，也就是说，它保留了所有信号结构，同时仅删除了由于组动作而造成的变化。在这里，我们证明了BNN能够在数据中发现任意的交换群体结构，并且训练有素的模型学习了组的不可减至表示，从而可以恢复组Cayley表。值得注意的是，受过训练的网络学会了对这些组的双偏见，因此具有分析对象的稳健性，完整性和通用性。

我们介绍了一种通过使用高维随机向量计算来识别说话者的方法。它的优势是简单和速度。只有1.02k的活动参数和128分钟的通过训练数据，我们在1,251位扬声器的Voxceleb1数据集上获得了前1位和前5个分数，为31％和52％。这与CNN模型相反，CNN模型需要数百万个参数和数量级较高的计算复杂性，仅在相互信息中衡量的判别功率2 $ \ times $获得的判别能力。额外的92秒训练和广义学习矢量量化（GLVQ）将分数提高到48％和67％。训练有素的分类器在5.7毫秒内分类1秒。所有处理均在标准基于CPU的机器上进行。

在视觉场景理解中，推断对象的位置及其刚性转换仍然是一个开放的问题。在这里，我们提出了一种使用有效的分解网络的神经形态解决方案，该解决方案基于三个关键概念：（1）基于矢量符号体系结构（VSA）的计算框架，带有复杂值值矢量； （2）分层谐振器网络（HRN）的设计，以处理视觉场景中翻译和旋转的非交换性质，而两者都被组合使用； （3）设计多室尖峰拟态神经元模型，用于在神经形态硬件上实现复杂值的矢量结合。 VSA框架使用矢量结合操作来产生生成图像模型，其中绑定充当了几何变换的模棱两可的操作。因此，场景可以描述为向量产物的总和，从而可以通过谐振器网络有效地分解以推断对象及其姿势。 HRN启用了分区体系结构的定义，其中矢量绑定是一个分区内的水平和垂直翻译，以及另一个分区内的旋转和缩放的定义。尖峰神经元模型允许将谐振网络映射到有效且低功耗的神经形态硬件上。在这项工作中，我们使用由简单的2D形状组成的合成场景展示了我们的方法，经历了刚性的几何变换和颜色变化。同伴论文在现实世界的应用程序方案中为机器视觉和机器人技术展示了这种方法。

基于流量的生成模型已成为一系列重要的无监督学习方法。在这项工作中，我们结合了重新归一化组（RG）的关键思想和稀疏的先验分布，以设计基于层次的生成模型RG-Flow，该模型可以在不同的图像尺度上分离图像的不同信息，并在每个量表上提取分离的表示。我们演示了我们的合成多尺度图像数据集和Celeba数据集的方法，表明该分散表示形式可以在不同尺度上对图像的语义操纵和样式混合。为了可视化潜在表示，我们引入了基于流量的模型的接受场，并表明RG-Flow的接受场与卷积神经网络相似。此外，我们通过稀疏的拉普拉斯分布代替了广泛采用的各向同性高斯先前分布，以进一步增强表示形式的分离。从理论的角度来看，与先前具有$ O（l^2）$复杂性的生成模型相比，我们提出的方法具有$ O（\ log l）$复杂性，可用于覆盖具有边缘长度$ l $的图像。

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.

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.