There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions. Our artificial scientists not only learn to answer given questions, but also continually invent new questions, by proposing hypotheses to be verified or falsified through potentially complex and time-consuming experiments, including thought experiments akin to those of mathematicians. While an artificial scientist expands its knowledge, it remains biased towards the simplest, least costly experiments that still have surprising outcomes, until they become boring. We present an empirical analysis of the automatic generation of interesting experiments. In the first setting, we investigate self-invented experiments in a reinforcement-providing environment and show that they lead to effective exploration. In the second setting, pure thought experiments are implemented as the weights of recurrent neural networks generated by a neural experiment generator. Initially interesting thought experiments may become boring over time.
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鉴于在特殊命令输入中编码的目标,目标条件的强化学习(RL)旨在学习最佳政策。在这里,我们研究了目标条件的神经网(NNS),这些神经网已经学会以特定于上下文特定的重量矩阵形式生成深度NN策略,类似于1990年代的快速体重程序员和其他方法。使用表单的上下文命令“生成实现预期回报的策略”,我们的NN生成器将对参数空间的强大探索与跨命令的概括相结合,以迭代地找到越来越更好的策略。体重共享的超级核武器和策略嵌入形式缩放了我们生成深度NN的方法。实验表明,单个学识渊博的政策生成器如何制定在培训过程中获得任何回报的政策。最后,我们在表现出竞争性能的一系列连续控制任务上评估了算法。我们的代码是公开的。
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学习评估和改善政策是加强学习(RL)的核心问题。传统的RL算法学习为单个策略定义的值函数。最近探索的竞争选择是学习许多策略的单个价值功能。在这里,我们结合了基于参数的价值函数的参与者批判性架构和策略评估网络的策略嵌入,以学习评估(并从而有助于改善)的单个价值函数,以改善深度神经网络(NN)代表的任何策略。该方法产生竞争性的实验结果。在无限多个状态的连续控制问题中,我们的价值函数通过同时学习一小部分“探测状态”和从探测状态在策略返回中产生的动作的映射来最大程度地减少其预测错误。该方法以极少数状态的形式提取有关环境的重要抽象知识,足以完全指定许多政策的行为。策略仅通过改变探测状态的动作,遵循值函数的预测的梯度来改善。令人惊讶的是,只有通过分别知道如何在3和5的5个这样的国家中采取行动,才有可能克隆在游泳者V3和Hopper-V3环境中近乎最佳政策的行为。值得注意的是,我们经过评估NN策略的培训的价值功能也与政策体系结构的变化也不变:我们表明,它允许零拍学习线性策略的竞争力与培训中最佳政策竞争。我们的代码是公开的。
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我们来看看如何机器学习是获得独立的媒体集合中的项目的性质技术可用于自动嵌入故事写成这样的集合。要做到这一点,我们使用抽取歌曲的节奏,使音乐播放列表遵循叙事弧模型。我们的工作规定了一个开源的工具,使用预训练神经网络模型,以提取一组原始音频文件的全球节奏和应用这些措施,创造一个叙事的播放清单。此工具可在https://github.com/dylanashley/playlist-story-builder/releases/tag/v1.0.0
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Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.
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Explainable AI transforms opaque decision strategies of ML models into explanations that are interpretable by the user, for example, identifying the contribution of each input feature to the prediction at hand. Such explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by finding relevant subspaces in activation space that can be mapped to more abstract human-understandable concepts and enable a joint attribution on concepts and input features. To automatically extract the desired representation, we propose new subspace analysis formulations that extend the principle of PCA and subspace analysis to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), optimize relevance of projected activations rather than the more traditional variance or kurtosis. This enables a much stronger focus on subspaces that are truly relevant for the prediction and the explanation, in particular, ignoring activations or concepts to which the prediction model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.
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Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
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A statistical ensemble of neural networks can be described in terms of a quantum field theory (NN-QFT correspondence). The infinite-width limit is mapped to a free field theory, while finite N corrections are mapped to interactions. After reviewing the correspondence, we will describe how to implement renormalization in this context and discuss preliminary numerical results for translation-invariant kernels. A major outcome is that changing the standard deviation of the neural network weight distribution corresponds to a renormalization flow in the space of networks.
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We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we show that our method retrieves annotations that are at least as accurate as manual annotations, and can thus be used as ground truth without the burden of manually annotating 3D data. We do this using an analysis-by-synthesis approach, which compares renderings of the CAD models with the captured scene. We introduce a 'cloning procedure' that identifies objects that have the same geometry, to annotate these objects with the same CAD models. This allows us to obtain complete annotations for the ScanNet dataset and the recent ARKitScenes dataset.
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This article presents a novel review of Active SLAM (A-SLAM) research conducted in the last decade. We discuss the formulation, application, and methodology applied in A-SLAM for trajectory generation and control action selection using information theory based approaches. Our extensive qualitative and quantitative analysis highlights the approaches, scenarios, configurations, types of robots, sensor types, dataset usage, and path planning approaches of A-SLAM research. We conclude by presenting the limitations and proposing future research possibilities. We believe that this survey will be helpful to researchers in understanding the various methods and techniques applied to A-SLAM formulation.
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