Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bio-inspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.

In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process as training complex models over large datasets are expensive and time-consuming. Such a problem is even more evident when dealing with video analysis. Some works have considered transfer learning or domain adaptation, i.e., approaches that map the knowledge from one domain to another, to ease the training burden, yet most of them operate over individual or small blocks of frames. This paper proposes a novel approach to map the knowledge from action recognition to event recognition using an energy-based model, denoted as Spectral Deep Belief Network. Such a model can process all frames simultaneously, carrying spatial and temporal information through the learning process. The experimental results conducted over two public video dataset, the HMDB-51 and the UCF-101, depict the effectiveness of the proposed model and its reduced computational burden when compared to traditional energy-based models, such as Restricted Boltzmann Machines and Deep Belief Networks.

通常，基于生物谱系的控制系统可能不依赖于各个预期行为或合作适当运行。相反，这种系统应该了解未经授权的访问尝试的恶意程序。文献中提供的一些作品建议通过步态识别方法来解决问题。这些方法旨在通过内在的可察觉功能来识别人类，尽管穿着衣服或配件。虽然该问题表示相对长时间的挑战，但是为处理问题的大多数技术存在与特征提取和低分类率相关的几个缺点，以及其他问题。然而，最近的深度学习方法是一种强大的一组工具，可以处理几乎任何图像和计算机视觉相关问题，为步态识别提供最重要的结果。因此，这项工作提供了通过步态认可的关于生物识别检测的最近作品的调查汇编，重点是深入学习方法，强调他们的益处，暴露出弱点。此外，它还呈现用于解决相关约束的数据集，方法和体系结构的分类和表征描述。

Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory requirements hinder their applicability to large datasets, many approximate solvers were recently developed for scaling up kernel methods, such as random Fourier features. However, these scalable approaches are based on approximations of isotropic kernels, which are incapable of removing the influence of possibly irrelevant features. In this work, we design random Fourier features for automatic relevance determination kernels, widely used for variable selection, and propose a new method based on joint optimization of the kernel machine parameters and the kernel relevances. Additionally, we present a new optimization algorithm that efficiently tackles the resulting objective function, which is non-convex. Numerical validation on synthetic and real-world data shows that our approach achieves low prediction error and effectively identifies relevant predictors. Our solution is modular and uses the PyTorch framework.

Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer vision tasks, as they apply to datasets presenting symmetries (e.g. scalings, rotations, translations). The method of moving frames is classical for deriving operators invariant to the action of a Lie group in a manifold.Recently, a rotation and translation equivariant neural network for image data was proposed based on the moving frames approach. In this paper we significantly improve that approach by reducing the computation of moving frames to only one, at the input stage, instead of repeated computations at each layer. The equivariance of the resulting architecture is proved theoretically and we build a rotation and translation equivariant neural network to process volumes, i.e. signals on the 3D space. Our trained model overperforms the benchmarks in the medical volume classification of most of the tested datasets from MedMNIST3D.

由于可以自主使用的广泛应用，无人驾驶汽车（UAV）一直脱颖而出。但是，他们需要智能系统，能够提供对执行多个任务的看法的更多了解。在复杂的环境中，它们变得更具挑战性，因为有必要感知环境并在环境不确定性下采取行动以做出决定。在这种情况下，使用主动感知的系统可以通过在发生位移时通过识别目标来寻求最佳下一个观点来提高性能。这项工作旨在通过解决跟踪和识别水面结构以执行动态着陆的问题来为无人机的积极感知做出贡献。我们表明，使用经典图像处理技术和简单的深度强化学习（DEEP-RL）代理能够感知环境并处理不确定性的情况，而无需使用复杂的卷积神经网络（CNN）或对比度学习（CL），我们的系统能够感知环境并处理不确定性（CL），我们的系统能够感知环境并处理不确定性。 。

滑坡在陡峭的斜坡上具有破坏性和反复发生的自然灾害，并代表了生命和财产的风险。了解遗物滑坡的位置对于了解其机制，更新库存图并改善风险评估至关重要。但是，在覆盖着雨林植被的热带地区，遗物滑坡映射很复杂。提出了一种新的CNN方法，用于半自动检测遗物滑坡，该检测使用由K均值聚类算法生成的数据集并具有预训练步骤。在预训练中计算的权重用于微调CNN训练过程。使用CBERS-4A WPM图像进行了建议和标准方法之间的比较。使用三个用于语义分割的CNN（U-NET，FPN，Linknet）带有两个增强数据集。总共测试了42种CNN组合。在测试的组合之间，精度和回忆的值非常相似。每种组合的召回率都高于75 \％，但是精度值通常小于20 \％。假阳性（FP）样品被称为这些低精度值的原因。提出的方法的预测更准确，正确检测到更多的滑坡。这项工作表明，在被雨林覆盖的区域发现遗物滑坡存在局限性，这主要与牧场的光谱响应与与\ textit {gleichenella sp。}蕨类植物的森林砍伐区域之间的相似性有关，通常用作lands斑scars的指示。

通常认为CNN能够使用有关其接收领域内不同对象（例如其定向关系）的上下文信息。但是，这种能力的性质和限制从未得到充分探索。我们使用经过训练的标准U-NET探索特定类型的关系〜-定向〜-，以优化分割的跨透镜损失函数。我们按照借口细分任务训练该网络，需要取得成功的方向关系推理，并指出，凭借足够的数据和足够大的接收领域，它成功地学习了所提出的任务。我们进一步探讨了网络通过分析方向关系受到干扰的方案，并表明网络已经学会了使用这些关系来推理。

在本文中，我们应用了一个多代理增强学习（MARL）框架，允许基站（BS）和用户设备（UES）共同学习频道访问策略及其在无线的多个访问方案中的信号。在此框架中，BS和UES是需要合作才能提供数据的增强剂学习（RL）代理。与无争议和基于争议的基线的比较表明，即使在高流量情况下，我们的框架在高速公路上也达到了卓越的性能，同时保持低碰撞率。研究了该方法的可伸缩性，因为它是MARL中的一个主要问题，本文提供了第一个结果以解决它。

The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines "weak" tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of a covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. We propose Gaussian processes Bayesian additive regression trees (GP-BART) as an extension of BART which assumes Gaussian process (GP) priors for the predictions of each terminal node among all trees. We illustrate our model on simulated and real data and compare its performance to traditional modelling approaches, outperforming them in many scenarios. An implementation of our method is available in the R package rGPBART available at: https://github.com/MateusMaiaDS/gpbart