图 书 馆
建造想象和推理的机器：深层生成模型的原理和应用Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models |
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| 课程网址: | http://videolectures.net/deeplearning2016_mohamed_generative_mode... |
| 主讲教师: | Shakir Mohamed |
| 开课单位: | 谷歌公司 |
| 开课时间: | 2016-08-23 |
| 课程语种: | 英语 |
| 中文简介: |  深度生成模型为无监督学习问题提供了一种解决方案，其中需要机器学习系统来发现隐藏在未标记数据流中的结构。因为它们是生成性的，所以这些模型可以形成一个丰富的图像世界：一种可以用来探索数据变化、推理世界结构和行为并最终用于决策的想象力。本教程着眼于我们如何使用深度生成模型构建具有想象力的机器学习系统、它们使概率推理的类型成为可能，以及它们可用于决策和行动的方式。 深度生成模型具有广泛的应用，包括密度估计、图像去噪和绘画、数据压缩、场景理解、表示学习、3D 场景构建、半监督分类和层次控制等。在探索了这些应用之后，我们将勾勒出生成模型的概况，绘制出三组模型：完全观察模型、转换模型和潜在变量模型。不同的模型需要不同的推理原则，我们将探索可用的不同选项。模型和推理的不同组合会产生不同的算法，包括自回归分布估计器、变分自动编码器和生成对抗网络。尽管我们将强调深度生成模型，尤其是潜在变量类，但本教程的目的是探索可在整个机器学习过程中使用的一般原则、工具和技巧。这些可重用的主题包括贝叶斯深度学习、变分逼近、无记忆和摊销推理以及随机梯度估计。最后，我们将重点介绍未讨论的主题，并想象生成模型的未来。 |
| 课程简介: | Deep generative models provide a solution to the problem of unsupervised learning, in which a machine learning system is required to discover the structure hidden within unlabelled data streams. Because they are generative, such models can form a rich imagery the world in which they are used: an imagination that can harnessed to explore variations in data, to reason about the structure and behaviour of the world, and ultimately, for decision-making. This tutorial looks at how we can build machine learning systems with a capacity for imagination using deep generative models, the types of probabilistic reasoning that they make possible, and the ways in which they can be used for decision making and acting. Deep generative models have widespread applications including those in density estimation, image denoising and in-painting, data compression, scene understanding, representation learning, 3D scene construction, semi-supervised classification, and hierarchical control, amongst many others. After exploring these applications, we'll sketch a landscape of generative models, drawing-out three groups of models: fully-observed models, transformation models, and latent variable models. Different models require different principles for inference and we'll explore the different options available. Different combinations of model and inference give rise to different algorithms, including auto-regressive distribution estimators, variational auto-encoders, and generative adversarial networks. Although we will emphasise deep generative models, and the latent-variable class in particular, the intention of the tutorial will be to explore the general principles, tools and tricks that can be used throughout machine learning. These reusable topics include Bayesian deep learning, variational approximations, memoryless and amortised inference, and stochastic gradient estimation. We'll end by highlighting the topics that were not discussed, and imagine the future of generative models. |
| 关 键 词: | 深度生成模型; 机器学习系统; 无监督学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-16：liyy |
| 最后编审: | 2021-06-16：liyy |
| 阅读次数: | 67 |