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Tolstoy on Aesthetics最新文献

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Alternative Theories 替代理论
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-10
H. Mounce
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引用次数: 0
The Problem 这个问题
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-3
H. Mounce
Learning online is essential for an agent to perform well in an ever-changing world. An agent has to learn online not only out of necessity — a non-stationary world might render past learning useless — but also because continual tracking in a temporally coherent world can result in better performance than a fixed solution. Despite the necessity of online learning, we have made little progress towards building robust online learning methods. More specifically, a scalable online representation learning method for neural network function approximators has remained elusive. In this thesis, I investigate the reasons behind this lack of progress. I propose the idea of online-aware representations – data representations explicitly optimized for online learning – and argue that existing representation learning methods do not learn such representations. I investigate if neural networks are capable of learning these representations. My results suggest that neural networks can indeed learn representations that are highly effective for online learning, but learning these representations online using gradient-based methods is challenging. More specifically, long-term credit assignment using back-propagation through time (BPTT) does not scale with the size of the problem. To address this, I propose Learning with Backtracking for slowly and continually improving representations online. The primary idea behind LwB is that while it is not possible to compute an accurate estimate of the representation update online, it is possible to verify if an update is useful online.
在线学习对于智能体在不断变化的世界中表现良好至关重要。智能体必须在线学习,这不仅是出于必要——一个非固定的世界可能会使过去的学习变得无用——而且还因为在一个暂时连贯的世界中持续跟踪可能会比固定的解决方案产生更好的性能。尽管在线学习是必要的,但我们在建立健全的在线学习方法方面进展甚微。更具体地说,神经网络函数逼近器的可扩展在线表示学习方法仍然难以捉摸。在本文中,我研究了这种缺乏进展的原因。我提出了在线感知表征的概念——明确为在线学习优化的数据表征——并认为现有的表征学习方法不能学习这种表征。我研究神经网络是否有能力学习这些表征。我的研究结果表明,神经网络确实可以学习对在线学习非常有效的表示,但是使用基于梯度的方法在线学习这些表示是具有挑战性的。更具体地说,使用时间反向传播(BPTT)的长期信用分配不能随问题的规模而扩展。为了解决这个问题,我建议用回溯学习来缓慢而持续地改进在线表示。LwB背后的主要思想是,虽然不可能在线计算对表示更新的准确估计,但可以在线验证更新是否有用。
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引用次数: 0
Art and Morality 艺术与道德
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-5
H. Mounce
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引用次数: 0
Shakespeare 莎士比亚
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-11
H. Mounce
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引用次数: 0
Art and Society 艺术与社会
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-2
H. Mounce
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引用次数: 0
The Central Features 中心特色
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-4
H. Mounce
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引用次数: 0
Summary and Elaboration 总结与阐述
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-9
H. Mounce
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引用次数: 0
Wagner 瓦格纳
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-8
H. Mounce
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引用次数: 0
The Background 背景
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-1
H. Mounce
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引用次数: 0
Art in History 历史中的艺术
Pub Date : 2019-07-12 DOI: 10.4324/9781315203225-6
H. Mounce
The program is designed to foster a lifelong interest in and sensitivity to the arts as a fundamental form of human expression and communication, equipping students with strong skills in a world in which visual literacy is vitally important. Based at one of the nation's top art schools, Tyler's art history major offers access to dynamic faculty members with a diverse range of interests; the resources of a large research university; study abroad programs in Rome and Tokyo; and opportunities for field trips, research, internships and enrichment in Philadelphia, a vibrant center of art, culture and history located near both New York and Washington, D.C.
该计划旨在培养对艺术的终身兴趣和敏感性,作为人类表达和交流的基本形式,使学生在视觉素养至关重要的世界中具备强大的技能。泰勒的艺术史专业位于美国顶尖的艺术学校之一,可以接触到兴趣广泛、充满活力的教师;大型研究型大学的资源;在罗马和东京的留学项目;以及实地考察、研究、实习和丰富费城的机会,费城是一个充满活力的艺术、文化和历史中心,靠近纽约和华盛顿特区
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引用次数: 0
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Tolstoy on Aesthetics
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