Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. Objective: A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs, including schizophrenia, schizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcare insurance claims and 2) electronic health records (EHRs). Design, setting and participants: Data from beneficiaries from a nationwide commercial healthcare insurer with 77.4 million members and data from patients from EHRs from eight academic hospitals based in the U.S. were used. First, the predictive models were constructed and tested using data in case-control cohorts from insurance claims or EHR data. Second, performance of the predictive models across data sources were analyzed. Third, as an illustrative application, the models were further trained to predict risks of SMIs among 18-year old young adults and individuals with substance associated conditions. Main outcomes and measures: Machine learning-based predictive models for SMIs in the general population were built based on insurance claims and EHR.

Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification.

This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.

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.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

需要在机器学习模型中对最小参数设置的需求，以避免耗时的优化过程。$ k $ - 最终的邻居是在许多问题中使用的最有效，最直接的模型之一。尽管具有众所周知的性能，但它仍需要特定数据分布的$ K $值，从而需要昂贵的计算工作。本文提出了一个$ k $ - 最终的邻居分类器，该分类器绕过定义$ k $的值的需求。考虑到训练集的数据分布，该模型计算$ k $值。我们将提出的模型与标准$ K $ - 最近的邻居分类器和文献中的两个无参数版本进行了比较。11个公共数据集的实验证实了所提出方法的鲁棒性，因为所获得的结果相似甚至更好。

季节预测$ \ unicode {x2013} $预测温度和降水量为2至6周$ \ unicode {x2013} $，对于有效的水分配，野火管理，干旱和缓解洪水至关重要。最近的国际研究工作提高了操作动力学模型的亚季节能力，但是温度和降水预测技能仍然很差，部分原因是代表动态模型内大气动力学和物理学的顽固错误。为了应对这些错误，我们引入了一种自适应偏置校正（ABC）方法，该方法将最新的动力学预测与使用机器学习的观察结合在一起。当应用于欧洲中等天气预测中心（ECMWF）的领先的亚季节模型时，ABC将温度预测技能提高了60-90％，在美国的连续美国，降水预测技能提高了40-69％基于Shapley队列的实用工作流程，用于解释ABC技能的提高并根据特定的气候条件识别机遇的高技能窗口。

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。 。

先前的工作表明，深-RL可以应用于无地图导航，包括混合无人驾驶空中水下车辆（Huauvs）的中等过渡。本文介绍了基于最先进的演员批评算法的新方法，以解决Huauv的导航和中型过渡问题。我们表明，具有复发性神经网络的双重评论家Deep-RL可以使用仅范围数据和相对定位来改善Huauvs的导航性能。我们的深-RL方法通过通过不同的模拟场景对学习的扎实概括，实现了更好的导航和过渡能力，表现优于先前的方法。

深钢筋学习中的确定性和随机技术已成为改善运动控制和各种机器人的决策任务的有前途的解决方案。先前的工作表明，这些深-RL算法通常可以应用于一般的移动机器人的无MAP导航。但是，他们倾向于使用简单的传感策略，因为已经证明它们在高维状态空间（例如基于图像的传感的空间）方面的性能不佳。本文在执行移动机器人无地图导航的任务时，对两种深-RL技术 - 深确定性政策梯度（DDPG）和软参与者（SAC）进行了比较分析。我们的目标是通过展示神经网络体系结构如何影响学习本身的贡献，并根据每种方法的航空移动机器人导航的时间和距离提出定量结果。总体而言，我们对六个不同体系结构的分析强调了随机方法（SAC）更好地使用更深的体系结构，而恰恰相反发生在确定性方法（DDPG）中。