几乎所有的艺术视觉模型都对图像旋转敏感。现有方法通常通过使用增强的培训数据来学习伪延迟，以弥补缺失的归纳偏见。除了资源要求数据通胀过程之外，预测通常概括。卷积神经网络固有的感应偏置允许通过作用于像素网格的水平和垂直轴的内核进行翻译等效。但是，这种感应性偏差不允许旋转模棱两可。我们提出了一种径向光束采样策略，以及在这些梁上运行的径向内核，以固有地融合了中心反转协方差。加上角度距离损耗，我们提出了一个基于径向光束的图像典型化模型，即短BIC。我们的模型允许最大的连续角度回归，并规范化了任意中心旋转的输入图像。作为一个预处理模型，这可以通过模型不合式旋转敏感的下游预测来实现旋转不变的视觉管道。我们表明，我们的端到端训练的角度回归器能够预测几个视觉数据集的连续旋转角度，即FashionMnist，CIFAR10，COIL100和LFW。

本文涉及可微分的动态模型，与神经过程理论一致，铸造大脑功能作为内部生成模型解释观察的分层改进。我们的工作扩展了基于梯度的预测编码的现有实现，具有自动分化，并允许对非线性状态参数化进行深度神经网络。基于梯度的预测编码通过优化从刺激传播到潜伏状态的精度加权预测误差，优化了每个层的推断状态和重量。预测向后流动，从潜在状态朝向下层。这里建议的模型优化了潜在状态的分层和动态预测。分层预测编码预期内容和分层结构。动态预测捕获编码内容的变化以及更高阶导数。分层和动态预测相互作用并解决相同潜在状态的不同方面。我们将模型应用于顺序数据的各种感知和规划任务，并显示其相互依赖。特别是，我们演示了如何在离散时间步骤中采样的并行地址中的抽样距离的抽样距离。我们讨论了放松线性层次结构的可能性，以满足具有紧急特性的更灵活的图形结构。我们将模型的颗粒结构与描述生物网络中的预测编码的规范微电路进行比较，并查看与Markov橡皮布的连接作为表征模块化的工具。最后一节草图为嵌套的时空层次结构中有效的感知和规划的想法。

我们目前PredProp，在神经网络中的权重，活动双向，并行和局部优化和精确的方法。 PredProp共同地址推理和学习，学习秤动态速率和权重梯度的损失函数的曲率通过优化预测误差精度。 PredProp优化上本地可用于每个层的预测误差和变量严格基于与随机梯度下降和错误向前传播网络参数。相邻层可优化共享活动变量，使得预测误差可以在网络中向前传播，而预测向后传播。该方法尽量减少消极自由能，或证据下界整个网络。我们表明，PredProp训练的网络类似于基于梯度的预测编码时的权重的邻国活动变量之间的数量是一个。对比相关的工作，PredProp概括朝任意深度的向后的连接和对任何深网络架构优化精度。由于预测误差精度和Fisher信息针对每一层之间的类比，PredProp实现自然梯度下降的一种形式。当优化DNN模型，逐层PredProp渲染模型的双向预测编码网络。另外DNNs可以参数化2个活动变量之间的权重。我们评估PredProp为简单的推理，学习并结合任务密集DNNs。我们证明了，没有在网络中一个明确的采样工序，PredProp实现变推理的形式，允许从少量的更复杂的任务和数据集，以今后的工作数据和假评估的学习解开的嵌入。

在机器学习中的局部更新规则和基于机器学习中的全局梯度的优化存在越来越大的融合。一种特别激励的连接是预测编码网络中本地知识优化与用于培训最先进的深层人工神经网络的错误反向验证算法之间的对应关系。在这里，我们专注于相关的预测编码网络中的精度加权与深神经网络的自然梯度下降算法之间的相关，但仍然很大程度上是探讨的联系。精确加权预测编码是一种有趣的候选者，用于缩放不确定性感知优化 - 特别是对于具有大参数空间的模型 - 由于其分布式性质以及渔民信息度量的底层局部近似，自适应学习自然梯度下降的速率。在这里，我们表明，具有学习精度的分层预测编码网络确实能够解决具有与天然梯度的全局反向化的性能相当的各种监督和无监督的学习任务，并且优于其经典梯度下降对应对方，其中嵌入了高量噪声的任务或标签输入。当应用于未经监视的图像输入的自动编码时，确定性网络产生分层组织和解散的嵌入，暗示在预测编码和分层变分或分化推理之间的密切连接处。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected. The annotation quality might be affected by various aspects like annotation instructions, Human Intelligence Task (HIT) design, and wages paid to annotators, etc. To avoid potentially low-quality annotations which could mislead the evaluation of automatic summarization system outputs, we investigate the recruitment of high-quality MTurk workers via a three-step qualification pipeline. We show that we can successfully filter out bad workers before they carry out the evaluations and obtain high-quality annotations while optimizing the use of resources. This paper can serve as basis for the recruitment of qualified annotators in other challenging annotation tasks.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

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.