表示学习是通过利用训练数据来学习得到向量表示,这可以克服人工方法的局限性。 表示学习通常可分为两大类,无监督和有监督表示学习。大多数无监督表示学习方法利用自动编码器(如去噪自动编码器和稀疏自动编码器等)中的隐变量作为表示。 目前出现的变分自动编码器能够更好的容忍噪声和异常值。 然而,推断给定数据的潜在结构几乎是不可能的。 目前有一些近似推断的策略。 此外,一些无监督表示学习方法旨在近似某种特定的相似性度量。提出了一种无监督的相似性保持表示学习框架,该框架使用矩阵分解来保持成对的DTW相似性。 通过学习保持DTW的shaplets,即在转换后的空间中的欧式距离近似原始数据的真实DTW距离。有监督表示学习方法可以利用数据的标签信息,更好地捕获数据的语义结构。 孪生网络和三元组网络是目前两种比较流行的模型,它们的目标是最大化类别之间的距离并最小化了类别内部的距离。

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如何学习良好的潜在表示是现代机器学习时代的一个重要课题。对于强化学习,使用一个好的表示使决策过程更加有效。本次演讲,我将介绍我们的工作,构建基于任务的潜在操作空间,用于基于搜索的黑盒函数优化,寻找策略变更的表示,该表示支持在不完全信息协同博弈中联合策略搜索,以及不同的表示如何影响RL探索。

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https://www.youtube.com/watch?v=sH4a2a0ntUA

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This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relations among users/items for collaborative filtering (CF) tasks. HNCR contains two major phases: neighbor construction and recommendation framework. The first phase introduces a neighbor construction strategy to construct a semantic neighbor set for each user and item according to the user-item historical interaction. In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation. Via a series of extensive experiments, we show that HNCR outperforms its Euclidean counterpart and state-of-the-art baselines.

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This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relations among users/items for collaborative filtering (CF) tasks. HNCR contains two major phases: neighbor construction and recommendation framework. The first phase introduces a neighbor construction strategy to construct a semantic neighbor set for each user and item according to the user-item historical interaction. In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation. Via a series of extensive experiments, we show that HNCR outperforms its Euclidean counterpart and state-of-the-art baselines.

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