人类的活动识别在医学和监视领域具有重要意义。基于捕获的微多普勒（{\ mu} -d）签名，雷达对该字段表示了很大的可行性。在本文中，MIMO雷达用于制定用于非切向方案中的角速度（{\ mu} - {\ omega}）的新型微动谱图。组合{\ mu} -d和{\ mu} - {\ omega}签名表现出更好的性能。基于公制学习方法实现了88.9％的分类准确性。实验设置旨在捕获不同方面角度和视线（LOS）上的微观运动签名。与最先进的技术相比，利用的训练数据集具有较小的尺寸，其中捕获了八项活动。几次拍摄的学习方法用于调整预训练模型进行崩溃检测。最终模型显示了十项活动的分类准确性为86.42％。
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基于深度学习的人类识别的雷达已成为越来越兴趣的研究领域。已经表明，Micro Doppler（$ \ MU $ -D）可以通过捕获周期性的肢体微光来反映步行行为。主要方面之一是在考虑实时和培训数据集大小约束时最大化随附的类的数量。在本文中，使用多输入 - 多数输出（MIMO）雷达来制定高程角速度的微动光谱图（$ \ mu $ - $ \ $ \ omega $）。研究了将这种新型频谱图与常用$ \ mu $ -d连接的有效性。为了适应无约束的实际步行运动，使用自适应周期分割框架，并在半步态周期（$ \ $ \ $ 0.5 s）上训练了公制学习网络。研究了各种类级别（5--20），不同数据集大小和不同观察时间Windows 1--2 s的影响的研究。相对于雷达，收集了22名受试者的无约束步行数据集。拟议的几次学习（FSL）方法的分类误差为11.3％，每个受试者只有2分钟的培训数据。
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This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.
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2022-12-19
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a \emph{generative model}. We propose the AE-LSVI algorithm for best-policy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy \emph{uniformly} over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required.
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In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo datasets for the task, investigating how it affects the performance of our system. We present our results for the task along with extensive analysis of the generated comments with the aim of aiding future studies in feedback comment generation for English language learners.
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Foundation models can be disruptive for future AI development by scaling up deep learning in terms of model size and training data's breadth and size. These models achieve state-of-the-art performance (often through further adaptation) on a variety of tasks in domains such as natural language processing and computer vision. Foundational models exhibit a novel {emergent behavior}: {In-context learning} enables users to provide a query and a few examples from which a model derives an answer without being trained on such queries. Additionally, {homogenization} of models might replace a myriad of task-specific models with fewer very large models controlled by few corporations leading to a shift in power and control over AI. This paper provides a short introduction to foundation models. It contributes by crafting a crisp distinction between foundation models and prior deep learning models, providing a history of machine learning leading to foundation models, elaborating more on socio-technical aspects, i.e., organizational issues and end-user interaction, and a discussion of future research.
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Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone. Using the proposed method, we are the first to optimize such an autofocus-inspired algorithm based on analytical gradients. The algorithm achieves a reduction in MSE by 35.5 % and an improvement in SSIM by 12.6 % over the motion affected reconstruction. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.
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Temporal data like time series are often observed at irregular intervals which is a challenging setting for existing machine learning methods. To tackle this problem, we view such data as samples from some underlying continuous function. We then define a diffusion-based generative model that adds noise from a predefined stochastic process while preserving the continuity of the resulting underlying function. A neural network is trained to reverse this process which allows us to sample new realizations from the learned distribution. We define suitable stochastic processes as noise sources and introduce novel denoising and score-matching models on processes. Further, we show how to apply this approach to the multivariate probabilistic forecasting and imputation tasks. Through our extensive experiments, we demonstrate that our method outperforms previous models on synthetic and real-world datasets.
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在神经网络中，与任务相关的信息由神经元组共同表示。但是，对信息分布在单个神经元之间的特定方式尚不清楚：虽然部分只能从特定的单个神经元中获得，但其他部分是由多个神经元冗余或协同携带的。我们展示了部分信息分解（PID）是信息理论的最新扩展，可以解散这些贡献。由此，我们介绍了“代表性复杂性”的度量，该量度量化了访问跨多个神经元信息的难度。我们展示了这种复杂性如何直接适用于较小的层。对于较大的层，我们提出了子采样和粗粒程序，并证明了后者的相应边界。从经验上讲，为了量化解决MNIST任务的深度神经网络，我们观察到，代表性复杂性通过连续的隐藏层和过度训练都会降低。总体而言，我们建议代表性复杂性作为分析神经表示结构的原则且可解释的摘要统计量。
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背景：机器学习（ML）系统依靠数据来做出预测，与传统软件系统（例如数据处理管道，服务管道和模型培训）相比，该系统具有许多添加的组件。现有关于软件维护的研究研究了针对不同类型的问题（例如绩效和安全问题）的问题报告需求和解决过程。但是，ML系统具有特定的故障类别，报告ML问题需要特定于域的信息。由于ML和传统软件工程系统之间的特征不同，我们不知道报告需求在多大程度上不同，并且这些差异在多大程度上影响了问题解决过程。目的：我们的目标是调查ML和非ML问题之间分辨率时间的分布以及某些ML问题的分配时间是否存在差异。我们进一步研究了ML问题和非ML问题的修复大小。方法：我们在GitHub的最新活动应用ML项目中提取问题报告，提取请求和代码文件，并使用自动方法过滤ML和非ML问题。我们使用已知的深度学习错误分类法手动标记这些问题。我们测量了受控样本上ML和非ML问题的解决方案的分辨率时间和大小，并比较每个类别的分布。
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