人工神经网络(ANN)在解决经典和现代机器学习问题方面取得了显着成功。随着学习问题在规模和复杂性方面的增长,并扩展到多学科领域,将需要用于缩放人工神经网络的模块化方法。模块化神经网络(MNN)是体现模块概念和原理的神经网络。 MNN采用大量不同的技术来实现模块化。以前的模块化技术调查在对MNN的系统分析中相对较为重要,主要侧重于经验比较,缺乏广泛的分类框架。在这篇综述中,我们建立了一个可靠的分类法来捕捉MNN不同变体的基本属性和关系。基于对模块化技术起作用的不同层次的研究,我们尝试为研究MNN的理论家提供一个通用和系统的框架,同时强调不同模块化方法的优点和缺点,以突出神经网络从业者的良好实践。
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我们如何建立一个学习者能够捕捉到使问题变得比简单问题更复杂的本质,将特征线上的难题解决为它知道如何解决的小问题,并在解决较大的问题之前解决较小的问题?为了实现这一目标,我们专注于学习在具有组合和递归结构的特定问题族中进行推广:通过按顺序组合一组可重用的部分解决方案,可以找到它们的解决方案。我们的想法是将泛化问题重新定义为学习算法程序的问题:我们可以将这个家族的解决方案制定为关于代表之间转换的后续决策过程。我们的公式使学习者能够通过稀疏监督来学习自己的计算图的结构和参数,通过将一个问题表示转换为另一个问题表示来解决问题之间的类比,开发模块化和重用以扩展到不同复杂度的问题。解决各种多语言算法的实验问题证明我们的方法发现了将问题分层分解为其子问题,将分布推广到看不见的问题类,并推断出相同问题的更难版本,与单片复现神经网络相比,样本复杂度降低了10倍。
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深度神经网络(DNN)正在成为现代计算应用中的重要工具。加速他们的培训是一项重大挑战,技术范围从分布式算法到低级电路设计。在这项调查中,我们从理论的角度描述了这个问题,然后是并行化的方法。我们介绍了DNN体系结构的趋势以及由此产生的对并行化策略的影响。然后,我们回顾并模拟DNN中不同类型的并发性:从单个运算符,到网络推理和训练中的并行性,再到分布式深度学习。我们讨论异步随机优化,分布式系统架构,通信方案和神经架构搜索。基于这些方法,我们推断了在深度学习中并行性的潜在方向。
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本文档概述了斯坦福大学2018年春季课程所涵盖的课程。该课程借鉴了认知和系统神经科学的洞察力,实现了混合连接主义和符号推理系统,通过集成来利用和扩展最先进的机器学习人机智能。作为一个具体的例子,我们专注于从专家软件工程师的连续对话中学习的数字助理,同时提供初始值作为功率分析,计算和数学学者。随着时间的推移,这些savants通过学习他们的专业同事来学习认知策略(领域相关的问题解决技能)和发展理论(启发式和应用它们所需的经验)。通过这样做,这些学者提升了他们的分析能力,使他们能够在平等的基础上合作,成为兼容性的合作者 - 有效地作为认知扩展和数字假肢,从而放大和模仿他们的人类伙伴的概念灵活思维模式,并改善对强大计算的访问和控制资源。
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实现机器智能需要平滑地整合感知和推理,但迄今为止开发的模型往往专注于一个或另一个;迄今为止,从丰富的感知空间获得的符号的复杂操作已被证明是难以捉摸的。考虑一个视觉算术任务,其中目标是对在自然条件下呈现的数字执行简单的算术算法(例如,手写,随机放置)。我们建议使用两层架构来解决这个问题。较低层包括信息处理模块的异构集合,其可以包括用于从图像中定位和提取字符的预训练的深度神经网络,以及对通过感知提取的表示执行符号变换的模块。较高层由控制器组成,使用强化学习训练,协调模块以解决高级任务。例如,控制器可以了解执行感知网络的上下文以及应用于其输出的符号变换。由此产生的模型能够解决视觉算术领域中的各种任务,并且具有几个优点标准,架构均匀的前馈网络,包括提高的样本效率。
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当提供数据并通过梯度下降方法训练时,深度神经网络可以很好地逼近复杂的函数。同时,存在大量现有功能,这些功能以编程方式以精确的方式解决了不同的任务,从而消除了对培训的需求。在许多情况下,可以将任务分解为一系列功能,其中一些我们可能更喜欢使用神经网络来学习功能,而对于其他人来说,首选方法是使用现有的黑盒功能。我们提出了一种基础神经网络的端到端训练方法,该方法集成了对现有黑盒函数的调用。我们通过使用可区分的神经网络来实现黑盒功能,以便在端到端优化过程中驱动基础网络符合黑盒功能接口。在推理时,将可微分估计器与其外部黑盒子可微分对应物放置在一起,使得基本网络输出与黑盒函数的输入参数匹配。使用这种“估计和替换”范例,我们训练一个端到端的神经网络,以计算输入的toblack-box功能,同时消除对中间标签的需求。通过在推理期间利用现有的精确黑盒功能,集成模型比完全可微分的模型更好地概括,并且与基于RL的方法相比,学习更有效。
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组合性是解决组合复杂性和维度诅咒的关键策略。最近的工作表明,可以学习组合解决方案,并在各种领域提供实质性收益,包括多任务学习,语言建模,视觉问答,机器理解等。然而,当必须共同学习模块参数及其组成时,这些模型在训练期间呈现出独特的挑战。在本文中,我们确定了其中的几个问题并分析了它们的根本原因。我们的讨论侧重于网络,这个问题的一般方法,并根据经验检验这些挑战和各种设计决策的相互作用。特别地,我们考虑算法如何决定模块化,算法如何更新模块以及算法是否使用规则化的效果。
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该调查概述了自动系统的验证技术,重点关注安全关键的自治网络物理系统(CPS)及其子组件。 CPS中的自主性通过人工智能(AI)和机器学习(ML)的最新进展实现,通过诸如深度神经网络(DNN)的方法,嵌入在所谓的学习启用组件(LEC)中,完成从分类到控制的任务。最近,正式方法和形式验证社区已经开发出一些方法来描述这些LEC中的行为,其最终目标是正式验证LEC的规范,本文介绍了对这些最近方法的许多方法的调查。
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自动化规划是人工智能从一开始就是主要的研究领域之一。自动化规划研究旨在开发能够自动解决复杂问题的一般推荐者(即规划者)。从广义上讲,规划者依靠一个通用模型来描述世界的可能状态以及为改变世界地位而可以采取的行动。给定模型和初始已知状态,规划器的目标是合成实现特定目标状态所需的一组动作。经典的计划方法大致对应于上面给出的描述。基于时间轴的方法是一种特定的规划范例,能够在统一的求解过程中整合因果和时间推理。尽管缺少对相关规划概念的共同解释,但这种方法已成功应用于许多现实场景中。实际上,应用这种技术的现有框架之间存在显着差异。每个框架都依赖于自己对基于时间轴的规划的解释,因此比较这些系统并不容易。因此,这项工作的目的是通过解决从相关规划概念的语义到建模和求解技术的几个方面来研究基于时间线的规划方法。具体而言,该博士工作的主要贡献包括:(i)对基于时间线的方法进行非正式表征的提议,该方法能够处理时间不确定性; (ii)分层建模和解决方案的提议; (iii)制定一个用于规划与时间表的执行的通用框架; (iv)在现实世界的制造场景中验证这种方法的{\ dag}。
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We propose the neural programmer-interpreter (NPI): a recurrent and composi-tional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation , lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.
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深度神经网络是高效灵活的模型,可以很好地完成各种任务,如图像,语音识别和自然语言理解。特别是,卷积神经网络(CNN)在计算机视觉研究人员和更具体的分类任务中产生了浓厚的兴趣。 CNN架构和相关的超参数通常与处理任务的性质相关,因为网络提供了允许最佳收敛的复杂和相关特征。设计这样的架构需要大量的人类专业知识,大量的计算时间并且不总是导致最佳网络。模型配置主题已经在机器学习中进行了广泛的研究,而没有引入标准的自动方法。本次调查的重点是审查和讨论CNN架构搜索自动化的最新进展。
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The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on top of the PyTorch deep neural networks library, enabling fast CPU and GPU computation for large spiking networks. The BindsNET framework can be adjusted to meet the needs of other existing computing and hardware environments, e.g., TensorFlow. We also provide an interface into the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning problems. We argue that this package facilitates the use of spiking networks for large-scale machine learning experimentation, and show some simple examples of how we envision BindsNET can be used in practice.
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强化学习方法长期以来一直吸引着数据管理社区,因为它们能够学习如何从原始系统性能中控制动态行为。最近在深度神经网络与强化学习相结合方面取得的成功引发了人们对该领域的重大兴趣。然而,由于大量的训练数据需求,算法不稳定以及缺乏标准工具,实际解决方案仍然难以实现。在这项工作中,我们介绍了LIFT,一种端到端的软件堆栈,用于将深度强化学习应用于数据管理任务。虽然之前的工作经常探索模拟中的应用,但LIFT主要利用人类专业知识从示范中学习,从而减少在线培训时间。我们进一步介绍了TensorForce,一个TensorFlow库,用于应用深度强化学习,为常见的RL算法提供统一的声明性接口,从而为LIFT提供后端。我们在流处理中的数据库复合索引和资源管理的两个案例研究中证明了LIFT的实用性。结果显示,从演示中初始化的LIFT控制器可以在延迟度量和空间使用率方面优于人类基线和启发式,最高可达70%。
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对机器学习和深度学习自动化的兴趣日益增长,这不可避免地导致了用于神经结构优化的自动化方法的发展。事实证明,网络架构的选择至关重要,深度学习的许多进步源于其直接的改进。然而,深度学习技术是计算密集型的,并且它们的应用需要高水平的领域知识。因此,即使是这一过程的部分自动化,也有助于使研究人员和从业人员更容易进行深度学习。通过这项调查,我们提供了一种形式主义,它将现有方法的景观统一和分类,并通过详细分析比较和对比不同的方法。我们通过讨论基于执行学习和进化算法原理的通用架构搜索空间和架构优化算法以及包含代理和一次性模型的方法来实现这一目标。此外,我们还讨论了新的研究方向,包括约束和多目标架构搜索以及自动数据增强,优化器和激活功能搜索。
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Deep learning has been popularized by its recent successes on challenging artificial intelligence problems. One of the reasons for its dominance is also an ongoing challenge: the need for immense amounts of computational power. Hardware architects have responded by proposing a wide array of promising ideas, but to date, the majority of the work has focused on specific algorithms in somewhat narrow application domains. While their specificity does not diminish these approaches, there is a clear need for more flexible solutions. We believe the first step is to examine the characteristics of cutting edge models from across the deep learning community. Consequently, we have assembled Fathom: a collection of eight archetypal deep learning workloads for study. Each of these models comes from a seminal work in the deep learning community, ranging from the familiar deep convolutional neural network of Krizhevsky et al., to the more exotic memory networks from Facebook's AI research group. Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model. We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the behavior of the Fathom workloads. We present a breakdown of where time is spent, the similarities between the performance profiles of our models, an analysis of behavior in inference and training, and the effects of parallelism on scaling.
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人工智能(AI)的最新进展使人们重新建立了像人一样学习和思考的系统。许多进步来自于在对象识别,视频游戏和棋盘游戏等任务中使用端到端训练的深度神经网络,在某些方面实现了与人类相当的性能。尽管他们的生物灵感和性能成就,这些系统不同于人类智能的不规则方式。我们回顾了认知科学的进展,表明真正的人类学习和思维机器将不得不超越当前的工程学习趋势,以及他们如何学习它。具体而言,我们认为这些机器应该(a)构建世界的因果模型支持解释和理解,而不仅仅是解决模式识别问题; (b)在物理学和心理学的直觉理论中进行基础学习,以支持和丰富所学知识;以及(c)利用组合性和学习 - 学习快速获取知识并将其推广到新的任务和情境。我们建议针对这些目标的具体挑战和有希望的途径,这些目标可以将最近神经网络进步的强度与更结构化的认知模型结合起来。
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Composable Controllers for Physics-Based Character Animation An ambitious goal in the area of physics-based computer animation is the creation of virtual actors that autonomously synthesize realistic human motions and possess a broad repertoire of lifelike motor skills. To this end, the control of dynamic, anthropomorphic figures subject to gravity and contact forces remains a difficult open problem. We propose a framework for composing controllers in order to enhance the motor abilities of such figures. A key contribution of our composition framework is an explicit model of the "pre-conditions" under which motor controllers are expected to function properly. We demonstrate controller composition with preconditions determined not only manually, but also automatically based on Support Vector Machine (SVM) learning theory. We evaluate our composition framework using a family of controllers capable of synthesizing basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of standing up after a fall. We furthermore demonstrate these basic controllers working in conjunction with more dynamic motor skills within a two-dimensional and a three-dimensional prototype virtual stuntperson. Our composition framework promises to enable the community of physics-based animation practitioners to more easily exchange motor controllers and integrate them into dynamic characters. ii Dedication To my father, Nikolaos Faloutsos, my mother, Sofia Faloutsou, and my wife, Florine Tseu. iii Acknowledgements I am done! Phew! It feels great. I have to do one more thing and that is to write the acknowledgements, one of the most important parts of a PhD thesis. The educational process of working towards a PhD degree teaches you, among other things, how important the interaction and contributions of the other people are to your career and personal development. First, I would like to thank my supervisors, Michiel van de Panne and Demetri Terzopoulos, for everything they did for me. And it was a lot. You have been the perfect supervisors. THANK YOU! However, I will never forgive Michiel for beating me at a stair-climbing race during a charity event that required running up the CN Tower stairs. Michiel, you may have forgotten, but I haven't! I am grateful to my external appraiser, Jessica Hodgins, and the members of my supervisory committee, Ken Jackson, Alejo Hausner and James Stewart, for their contribution to the successful completion of my degree. I would like to thank my close collaborator, Victor Ng-Thow-Hing, for being the richest source of knowledge on graphics research, graphics technology, investing and martial arts movies. Too bad you do not like Jackie Chan, Victor. A great THANKS is due to Joe Laszlo, the heart and soul of our lab's community spirit. Joe practically ran our lab during some difficult times. He has spent hours of his time to ensure the smooth operation of the lab and its equip
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Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both: current ways of gener-ating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current "brand-names" of reservoir methods, and thus aims to help unifying the field and providing the reader with a detailed "map" of it.
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强化学习(RL)是机器学习的一个分支,用于解决各种顺序决策问题而无需预先监督。由于最近深度学习的进步,新提出的Deep-RL算法已经能够在复杂的高维环境中表现得非常好。然而,即使在许多领域取得成功之后,这些方法的主要挑战之一是与高效决策所需的环境的高度相互作用。从大脑中寻求灵感,这个问题可以通过偏置决策来结合基于实例的学习来解决。记录高级经验。本文回顾了各种最近的强化学习方法,它们结合了外部记忆来解决决策问题,并对它们进行了调查。我们概述了不同的方法 - 以及它们的优点和缺点,应用以及用于基于内存的模型的标准实验设置。该评论希望成为有用的资源,以提供该领域最新进展的关键见解,并为其未来的进一步发展提供帮助。
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