在机器学习中,生成模型可以用来直接对数据建模(例如根据某个变量的概率密度函数进行数据采样),也可以用来建立变量间的条件概率分布。条件概率分布可以由生成模型根据贝叶斯定理形成。

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监督学习在过去取得了巨大的成功,然而监督学习的研究进入了瓶颈期,因其依赖于昂贵的人工标签,却饱受泛化错误(generalization error)、伪相关(spurious correlations)和对抗攻击(adversarial attacks)的困扰。自监督学习以其良好的数据利用效率和泛化能力引起了人们的广泛关注。本文将全面研究最新的自监督学习模型的发展,并讨论其理论上的合理性,包括预训练语言模型(Pretrained Language Model,PTM)、生成对抗网络(GAN)、自动编码器及其拓展、最大化互信息(Deep Infomax,DIM)以及对比编码(Contrastive Coding)。自监督学习与无监督学习的区别主要在于,无监督学习专注于检测特定的数据模式,如聚类、社区发现或异常检测,而自监督学习的目标是恢复(recovering),仍处于监督学习的范式中。下图展示了两者之间的区别,自监督中的“related information” 可以来自其他模态、输入的其他部分以及输入的不同形式。

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Digital services have been offered through remote systems for decades. The questions of how these systems can be built in a trustworthy manner and how their security properties can be understood are given fresh impetus by recent hardware developments, allowing a fuller, more general, exploration of the possibilities than has previously been seen in the literature. Drawing on and consolidating the disparate strains of research, technologies and methods employed throughout the adaptation of confidential computing, we present a novel, dedicated Confidential Remote Computing (CRC) model. CRC proposes a compact solution for next-generation applications to be built on strong hardware-based security primitives, control of secure software products' trusted computing base, and a way to make correct use of proofs and evidence reports generated by the attestation mechanisms. The CRC model illustrates the trade-offs between decentralisation, task size and transparency overhead. We conclude the paper with six lessons learned from our approach, and suggest two future research directions.

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Digital services have been offered through remote systems for decades. The questions of how these systems can be built in a trustworthy manner and how their security properties can be understood are given fresh impetus by recent hardware developments, allowing a fuller, more general, exploration of the possibilities than has previously been seen in the literature. Drawing on and consolidating the disparate strains of research, technologies and methods employed throughout the adaptation of confidential computing, we present a novel, dedicated Confidential Remote Computing (CRC) model. CRC proposes a compact solution for next-generation applications to be built on strong hardware-based security primitives, control of secure software products' trusted computing base, and a way to make correct use of proofs and evidence reports generated by the attestation mechanisms. The CRC model illustrates the trade-offs between decentralisation, task size and transparency overhead. We conclude the paper with six lessons learned from our approach, and suggest two future research directions.

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