Efficient sampling of complex high-dimensional probability densities is a central task in computational science. Machine Learning techniques based on autoregressive neural networks have been recently shown to provide good approximations of probability distributions of interest in physics. In this work, we propose a systematic way to remove the intrinsic bias associated with these variational approximations, combining it with Markov-chain Monte Carlo in an automatic scheme to efficiently generate cluster updates, which is particularly useful for models for which no efficient cluster update scheme is known. Our approach is based on symmetry-enforced cluster updates building on the neural-network representation of conditional probabilities. We demonstrate that such finite-cluster updates are crucial to circumvent ergodicity problems associated with global neural updates. We test our method for first- and second-order phase transitions in classical spin systems, proving in particular its viability for critical systems, or in the presence of metastable states.
翻译:以自动递减神经网络为基础的机械学习技术最近被显示为提供了物理学关注的概率分布的良好近似值。在这项工作中,我们提出一个系统的方法来消除与这些变差近似值相关的内在偏差,将其与Markov-链-Monte Carlo 结合到一个自动计划中,以便有效地生成集束更新,这对于尚未知道高效集束更新计划的模型特别有用。我们的方法是以有条件概率神经网络代表为基础的对称强制集束更新。我们证明,这种定额集群更新对于避免与全球神经更新相关的惯性问题至关重要。我们测试了我们传统的旋转系统第一和第二阶段过渡的方法,特别证明了它对于关键系统的可行性,或元化状态的存在。