在医学中，精心策划的图像数据集经常采用离散标签来描述所谓的健康状况与病理状况的连续光谱，例如阿尔茨海默氏病连续体或图像在诊断中起关键点的其他领域。我们提出了一个基于条件变异自动编码器的图像分层的体系结构。我们的框架VAESIM利用连续的潜在空间来表示疾病的连续体并在训练过程中找到簇，然后可以将其用于图像/患者分层。该方法的核心学习一组原型向量，每个向量与群集关联。首先，我们将每个数据样本的软分配给群集。然后，我们根据样品嵌入和簇的原型向量之间的相似性度量重建样品。为了更新原型嵌入，我们使用批处理大小中实际原型和样品之间最相似表示的指数移动平均值。我们在MNIST手写数字数据集和名为Pneumoniamnist的医疗基准数据集上测试了我们的方法。我们证明，我们的方法在两个数据集中针对标准VAE的分类任务（性能提高了15％）的KNN准确性优于基准，并且还以完全监督的方式培训的分类模型同等。我们还展示了我们的模型如何优于无监督分层的当前，端到端模型。

与临床上建立的疾病类别相比，缺乏大型标记的医学成像数据集以及个体间的显着可变性，在精确医学范式中利用医学成像信息方面面临重大挑战个体预测和/或将患者分为较细粒的群体，这些群体可能遵循更多均匀的轨迹，从而赋予临床试验能力。为了有效地探索以无监督的方式探索医学图像中有效的自由度可变性，在这项工作中，我们提出了一个无监督的自动编码器框架，并增加了对比度损失，以鼓励潜在空间中的高可分离性。该模型在（医学）基准数据集上进行了验证。由于群集标签是根据集群分配分配给每个示例的，因此我们将性能与监督的转移学习基线进行比较。我们的方法达到了与监督体系结构相似的性能，表明潜在空间中的分离再现了专家医学观察者分配的标签。所提出的方法可能对患者分层有益，探索较大类或病理连续性的新细分，或者由于其在变化环境中的采样能力，因此医学图像处理中的数据增强。

由于其易于实施，多层感知器（MLP）在深度学习应用程序中变得无处不在。 MLP的基础图确实是多部分，即神经元的每个层仅连接到属于相邻层的神经元。在约束中，体内脑连接在单个突触的水平上表明，生物神经元网络的特征是无尺度度分布或指数截断的功率定律强度分布，这暗示了潜在的新型途径，用于开发进化衍生的神经元网络。在本文中，我们提出了“ 4ward”，这是一种方法和Python库，能够从任意复杂的定向无环形图中生成灵活有效的神经网络（NNS）。 4ward的灵感来自于从图形图纪律中绘制的分层算法以实现有效的向前传球，并在具有各种ERD \ H {O} S-R \'enyi图的计算实验中提供了显着的时间增长。 4Ward通过并行化激活的计算来克服学习矩阵方法的顺序性质，并为设计人员提供自定义权重初始化和激活函数的自由。我们的算法对于任何寻求在微观尺度的NN设计框架中利用复杂拓扑的研究者都可以帮助。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal models are not always perfect: for example, it is easy to underestimate a robot's inertia. Nevertheless, these models change and improve over time as humans gather more experience. Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change. In this work we take a step towards enabling robots to understand the influence they have, leverage it to better assist people, and help human models more quickly align with reality. Our key idea is to model the human's learning as a nonlinear dynamical system which evolves the human's internal model given new observations. We formulate a novel optimization problem to infer the human's learning dynamics from demonstrations that naturally exhibit human learning. We then formalize how robots can influence human learning by embedding the human's learning dynamics model into the robot planning problem. Although our formulations provide concrete problem statements, they are intractable to solve in full generality. We contribute an approximation that sacrifices the complexity of the human internal models we can represent, but enables robots to learn the nonlinear dynamics of these internal models. We evaluate our inference and planning methods in a suite of simulated environments and an in-person user study, where a 7DOF robotic arm teaches participants to be better teleoperators. While influencing human learning remains an open problem, our results demonstrate that this influence is possible and can be helpful in real human-robot interaction.

Explainability is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the topic, yet explainability still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the product of evidence stemming from the model and its input-output and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's decision-making) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are ope rationalized and provide new insight into common explanation methods that we analyze as case studies.

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by experts in the field, which makes it a labor-intensive and error-prone process. Thus, there is an arising need for automation in the process of fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.

Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across numerous domains. Their underlying ability to represent nodes as summaries of their vicinities has proven effective for homophilous graphs in particular, in which same-type nodes tend to connect. On heterophilous graphs, in which different-type nodes are likely connected, GNNs perform less consistently, as neighborhood information might be less representative or even misleading. On the other hand, GNN performance is not inferior on all heterophilous graphs, and there is a lack of understanding of what other graph properties affect GNN performance. In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance. To overcome these limitations, we introduce 2-hop Neighbor Class Similarity (2NCS), a new quantitative graph structural property that correlates with GNN performance more strongly and consistently than alternative metrics. 2NCS considers two-hop neighborhoods as a theoretically derived consequence of the two-step label propagation process governing GCN's training-inference process. Experiments on one synthetic and eight real-world graph datasets confirm consistent improvements over existing metrics in estimating the accuracy of GCN- and GAT-based architectures on the node classification task.

In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent's learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent's policy.

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-off between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.