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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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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数据分布通常会随着时间的流逝而发展，导致概念漂移等问题通常会降低分类器的性能。当前的技术不足以解决此问题，因为它们要么需要详细的转换知识，要么不适合预测看不见的域，而只能适应可用数据示例的域。我们试图预测看不见的数据（及其标签），使我们能够以主动的方式应对挑战，而不是检测并对已经导致错误的现有变化做出反应。为此，我们以无监督的方式学习了一个域变压器，允许生成看不见的域数据。我们的方法首先匹配了使用自动编码器获得的两个给定域的独立学习的潜在表示。反过来，可以学习原始样品的转换，可以迭代地应用以推断到看不见的域。我们对图像数据的CNN的评估证实了该方法的有用性。它还在无监督的域适应性问题上取得了非常好的结果，在该问题中，只有标签，但必须预测样品。代码可从https://github.com/johntailor/dotra获得。
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虽然对自动化简单任务的深度学习（DL）的潜力已经很好地探索，但最近的研究开始调查使用深度学习的创造性设计，既可以在创建过程中创建和支持人类。在本文中，我们使用计算创造力的见解来概念化和评估生成深入学习在文献综述中所确定的创意域中的当前应用。我们突出了当前系统之间的相似之处和不同型号的人类创造力以及它们的缺点。虽然深度学习产生高价值的结果，例如高质量的图像，但由于多种原因，它们的新颖性通常受到限制，因为多种原因如此涉及由训练数据和人类定义的概念性空间。当前DL方法也不允许内部问题表示的变化，并且它们缺乏识别高度不同域的连接的能力，这两者都被视为人类创造力的主要驱动因素。
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人工智能（AI）治理调节行使权威和控制AI的管理。它旨在通过有效利用数据并最大程度地减少与AI相关的成本和风险来利用AI。尽管AI治理和AI伦理等主题在理论，哲学，社会和监管层面上进行了详尽的讨论，但针对公司和公司的AI治理工作有限。这项工作将AI产品视为系统，在该系统中，通过机器学习（ML）模型（培训）数据传递关键功能。我们通过在AI和相关领域（例如ML）合成文献来得出一个概念框架。我们的框架将AI治理分解为数据的治理，（ML）模型和（AI）系统沿着四个维度。它与现有的IT和数据治理框架和实践有关。它可以由从业者和学者都采用。对于从业者来说，主要是研究论文的综合，但从业者的出版物和监管机构的出版物也为实施AI治理提供了宝贵的起点，而对于学者来说，该论文强调了许多AI治理领域，值得更多关注。
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人工智能（AI）具有很大的机会，但也可能构成重大风险。自动生成的决策解释可以提高透明度和促进信任，特别是对于基于AI模型的自动预测的系统。但是，给予，例如，造成不诚实的AI的经济激励措施，我们可以在多大程度上信任解释？为了解决这个问题，我们的工作调查了AI模型（即，深入学习和提高关于AI决定的透明度的现有仪器）如何创造和检测欺骗性解释。作为一个实证评估，我们专注于文本分类，并改变毕业的解释，是神经网络中熟悉的解释技术。然后，我们评估欺骗性解释对200名参与者的实验中的用户的影响。我们的调查结果证实，欺骗性解释确实可以愚弄人类。然而，可以部署机器学习（ML）方法以检测看似轻微的欺骗尝试，以超过足够的域知识超过80％。没有领域知识，仍然可以以无人监督的方式在解释中推断出不一致的，因为审查了预测模型的基本知识。
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Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.
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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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Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.
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Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system.
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