The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latentvariable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy or undercomplete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.

Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets.The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. We recast this approach as tensor factorisation by exploiting the tensor structure of standard neighbour density based descriptors. In doing so, we form compact tensor-reduced representations whose size does not depend on the number of chemical elements, but remain systematically convergeable and are therefore applicable to a wide range of data analysis and regression tasks.

深度学习方法正在成为高能量物理（HEP）中数据分析的首选方法。尽管如此，大多数以物理启发的现代体系结构在计算上效率低下，缺乏解释性。JET标记算法尤其如此，考虑到现代粒子探测器产生的大量数据，计算效率至关重要。在这项工作中，我们为喷气式代表介绍了一个新颖，多功能和透明的框架。Lorentz Group Boosts不变，这在喷气标记基准测试基准方面具有很高的精度，同时比其他现代方法更快地训练和评估了训练和评估。

本文提出了一个算法框架，用于控制符合信号时间逻辑（STL）规范的连续动力系统的合成。我们提出了一种新型算法，以从STL规范中获得时间分配的有限自动机，并引入一个多层框架，该框架利用此自动机以空间和时间上指导基于采样的搜索树。我们的方法能够合成非线性动力学和多项式谓词功能的控制器。我们证明了算法的正确性和概率完整性，并说明了我们在几个案例研究中框架的效率和功效。我们的结果表明，在艺术状态下，速度的速度是一定的。

在计算化学和材料科学中，创建快速准确的力场是一项长期挑战。最近，已经证明，几个直径传递神经网络（MPNN）超过了使用其他方法在准确性方面构建的模型。但是，大多数MPNN的计算成本高和可伸缩性差。我们建议出现这些局限性，因为MPNN仅传递两体消息，从而导致层数与网络的表达性之间的直接关系。在这项工作中，我们介绍了MACE，这是一种使用更高的车身订单消息的新型MPNN模型。特别是，我们表明，使用四体消息将所需的消息传递迭代数减少到\ emph {两}，从而导致快速且高度可行的模型，达到或超过RMD17的最新准确性，3BPA和ACAC基准任务。我们还证明，使用高阶消息会导致学习曲线的陡峭程度改善。