我们使用深层部分最小二乘（DPL）来估算单个股票收益的资产定价模型，该模型以灵活而动态的方式利用调理信息，同时将超额回报归因于一小部分统计风险因素。新颖的贡献是解决非线性因子结构，从而推进经验资产定价中深度学习的当前范式，该定价在假设高斯资产回报和因素的假设下使用线性随机折现因子。通过使用预测的最小二乘正方形来共同投影公司特征和资产回报到潜在因素的子空间，并使用深度学习从因子负载到资产回报中学习非线性图。捕获这种非线性风险因素结构的结果是通过线性风险因素暴露和相互作用效应来表征资产回报中的异常情况。因此，深度学习捕获异常值的众所周知的能力，在潜在因素结构中的角色和高阶项在因素风险溢价上的作用。从经验方面来说，我们实施了DPLS因子模型，并表现出比Lasso和Plain Vanilla深度学习模型表现出卓越的性能。此外，由于DPL的更简约的架构，我们的网络培训时间大大减少了。具体而言，在1989年12月至2018年1月的一段时间内使用Russell 1000指数中的3290资产，我们评估了我们的DPLS因子模型，并生成比深度学习大约1.2倍的信息比率。 DPLS解释了变化和定价错误，并确定了最突出的潜在因素和公司特征。

Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state-ofthe-art of deep learning from a modeling and algorithmic perspective. We provide a list of successful areas of applications in Artificial Intelligence (AI), Image Processing, Robotics and Automation. Deep learning is predictive in its nature rather then inferential and can be viewed as a black-box methodology for high-dimensional function estimation.

病理学家拥有丰富的词汇，他们可以描述细胞形态的所有细微差别。在他们的世界中，图像和单词都有自然的配对。最近的进步表明，现在可以对机器学习模型进行培训，以学习高质量的图像功能并将其表示为离散信息。这使得自然语言（也是离散的语言）可以与成像旁边共同建模，从而描述了成像内容。在这里，我们介绍了将离散建模技术应用于非黑色素瘤皮肤癌的问题结构域，特别是eme骨内癌（IEC）的组织学图像。通过实施IEC图像的高分辨率（256x256）图像的VQ-GAN模型，我们训练了序列到序列变压器，以使用病理学家术语来生成自然语言描述。结合使用连续生成方法获得的交互式概念矢量的概念，我们展示了一个额外的解释性角度。结果是为高度表达的机器学习系统而努力的一种有希望的方法，不仅可以用作预测/分类工具，而且还意味着要进一步了解我们对疾病的科学理解。

制定了具有机器学习模拟（骆驼）项目的宇宙学和天体物理学，通过数千名宇宙的流体动力模拟和机器学习将宇宙学与天体物理学结合起来。骆驼包含4,233个宇宙学仿真，2,049个n-body和2,184个最先进的流体动力模拟，在参数空间中采样巨大的体积。在本文中，我们介绍了骆驼公共数据发布，描述了骆驼模拟的特性和由它们产生的各种数据产品，包括光环，次麦，银河系和空隙目录，功率谱，Bispectra，Lyman - $ \ Alpha $光谱，概率分布函数，光环径向轮廓和X射线光子列表。我们还释放了超过骆驼 - 山姆的数十亿个星系的目录：与Santa Cruz半分析模型相结合的大量N身体模拟。我们释放包含350多个Terabytes的所有数据，并包含143,922个快照，数百万光环，星系和摘要统计数据。我们提供有关如何访问，下载，读取和处理数据AT \ URL {https://camels.readthedocs.io}的进一步技术详细信息。

利益的现实世界任务通常由人类可读描述定义不足，并且没有预定义的奖励信号，除非它由人类设计师定义。相反，数据驱动的算法通常旨在解决特定的，狭义定义的任务，具有驱动代理学习的性能度量。在这项工作中，我们提出了赢得第一名的解决方案，并获得了2021个神经潮端竞赛Minerl Basalt挑战的最人性化的代理：从Minecraft中的人力反馈中学习，该参与者使用人类数据来解决仅限定义的四个任务通过自然语言描述，没有奖励功能。我们的方法使用可用的人类演示数据来培训仿制学习策略，以便导航和额外的人机反馈来训练图像分类器。然后将这些模块与估计的内径型图一起组合到基于人类的人类知识设计的状态机，该任务在自然等级中断和控制学习代理应该在任何瞬间遵循的宏观行为的控制中。我们将这种混合智能方法与端到端机器学习和纯工程解决方案进行比较，然后由人类评估符判断。 CodeBase可在https://github.com/viniciusguigo/kairos_minerl_basalt上获得。

医疗AI通过支持基于证据的医学实践，个性化患者治疗，降低成本以及改善提供者和患者体验，推进医疗保健的巨大潜力。我们认为解锁此潜力需要一种系统的方法来衡量在大规模异构数据上的医疗AI模型的性能。为了满足这种需求，我们正在建立Medperf，这是一个开放的框架，用于在医疗领域的基准测试机器学习。 Medperf将使联合评估能够将模型安全地分配给不同的评估设施，从而赋予医疗组织在高效和人类监督过程中评估和验证AI模型的性能，同时优先考虑隐私。我们描述了当前的挑战医疗保健和AI社区面临，需要开放平台，Medperf的设计理念，其目前的实施状态和我们的路线图。我们呼吁研究人员和组织加入我们创建Medperf开放基准平台。

从开放式网络策略的大规模未过滤数据集培训的语言模型获取从其培训数据的系统偏差，偏见和有害视图。我们提出了一种从Web级数据集上以编程方式识别和删除有害文本的方法。预先训练的语言模型用于计算在特定文档上调节的研究员写入触发短语的日志可能性，该语言用于从数据集中识别和过滤文档。我们证明，在该过滤的数据集上培训的模型表现出较低的倾向，以产生有害文本，与未过滤的基线相比，标准语言建模基准的性能下降了下降。通过从标准语言建模基准测试的讨论语音和其他不良内容的介绍来提供对这种性能差异的部分解释。最后，我们讨论了这种方法的概括以及如何通过研究人员使用反映特定值的触发短语来构建与其值更紧密对齐的语言模型。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.