摘要翻译:
我们研究了弱监督数据集的参数估计问题,其中训练样本由输入和部分指定的注释组成,我们称之为输出。注释中缺少的信息使用潜在变量建模。以往的方法给单一分布带来了两个不同的任务:(i)在训练过程中建模潜在变量中的不确定性;以及(ii)在测试过程中对输出和潜在变量做出准确的预测。我们提出了一个新的框架,利用两个分布来分离两个任务的需求:(i)一个条件分布来建模给定投入产出对的潜在变量的不确定性;和(ii)用于预测给定输入的输出和潜在变量的delta分布。在学习过程中,我们通过最小化基于损失的相异系数来鼓励两个分布之间的一致性。我们的方法在两个重要方面推广了潜在支持向量机:(i)它对潜在变量的不确定性建模,而不是依赖于逐点估计;并且(ii)它允许使用依赖于潜在变量的损失函数,这大大增加了它的适用性。我们演示了我们的方法在两个具有挑战性的问题上的有效性--对象检测和动作检测--使用公开可用的数据集。
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英文标题:
《Modeling Latent Variable Uncertainty for Loss-based Learning》
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作者:
M. Pawan Kumar (Ecole Centrale Paris), Ben Packer (Stanford
University), Daphne Koller (Stanford University)
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最新提交年份:
2012
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Machine Learning 机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述:Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要:
We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.
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PDF链接:
https://arxiv.org/pdf/1206.4636