用短逻辑公式大幅压缩概率分布的存储空间。
A First Step Towards Even More Sparse Encodings of Probability Distributions
- 从概率分布中提取一阶逻辑公式，减少存储值数量。
- 实验显示仅用少量短公式即可大幅提升编码稀疏性。
- 适合需要高效存储和泛化概率模型的研究者。
现实场景可用提升后的概率分布建模，但通常以表格或列表形式编码，需指数级存储量。为此，我们提出一种方法：先减少分布中的数值数量，再为每个值提取逻辑公式并进一步最小化。该过程在提升编码稀疏性的同时增强分布泛化能力。评估表明，通过提取少量短公式，可实现极高的稀疏性，同时保留核心信息。
原文摘要 · Abstract (English)
Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information.
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