带分数匹配的乘积雅可比-西塔玻尔兹曼机
Product Jacobi-Theta Boltzmann machines with score matching
AI总结：
本文提出乘积雅可比-西塔玻尔兹曼机（pJTBM），作为RTBM的受限版本，并证明基于Fisher散度的分数匹配能以更高效率拟合概率密度。
摘要：
概率密度函数的估计是一项非平凡的任务，近年来已借助机器学习技术加以解决。使用受玻尔兹曼机（BM）架构启发的模型可以获得成功的应用。本文引入了乘积雅可比-西塔玻尔兹曼机（pJTBM），它是黎曼-西塔玻尔兹曼机（RTBM）的一种受限版本，其隐藏层连接矩阵为对角矩阵。我们证明，基于Fisher散度的分数匹配可以比原始RTBM更高效地用于拟合pJTBM的概率密度。
英文摘要：
The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques. Successful applications can be obtained using models inspired by the Boltzmann machine (BM) architecture. In this manuscript, the product Jacobi-Theta Boltzmann machine (pJTBM) is introduced as a restricted version of the Riemann-Theta Boltzmann machine (RTBM) with diagonal hidden sector connection matrix. We show that score matching, based on the Fisher divergence, can be used to fit probability densities with the pJTBM more efficiently than with the original RTBM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
