Probabilistic inference in high-dimensional state-space models is
computationally challenging. For many spatiotemporal systems, however, prior
knowledge about the dependency structure of state variables is available. We
leverage this structure to develop a computationally efficient approach to
state estimation and learning in graph-structured state-space models with
(partially) unknown dynamics and limited historical data. Building on recent
methods that combine ideas from deep learning with principled inference in
Gaussian Markov random fields (GMRF), we reformulate graph-structured
state-space models as Deep GMRFs defined by simple spatial and temporal graph
layers. This results in a flexible spatiotemporal prior that can be learned
efficiently from a single time sequence via variational inference. Under linear
Gaussian assumptions, we retain a closed-form posterior, which can be sampled
efficiently using the conjugate gradient method, scaling favourably compared to
classical Kalman filter based approaches

该论文提出了一种基于图结构状态空间模型的概率推理方法，利用深度学习和高斯马尔可夫随机场的有原则的推理方法，定义简单的空间和时间图层，并通过变分推理从单个时间序列中高效地学习出灵活的时空先验分布，可缩放地采样出闭合的后验。

基于深度高斯马尔科夫随机场的图结构动态系统

Deep Gaussian Markov Random Fields for Graph-Structured Dynamical  Systems

Planning - the ability to analyze the structure of a problem in the large and
decompose it into interrelated subproblems - is a hallmark of human
intelligence. While deep reinforcement learning (RL) has shown great promise
for solving relatively straightforward control tasks, it remains an open
problem how to best incorporate planning into existing deep RL paradigms to
handle increasingly complex environments. One prominent framework, Model-Based
RL, learns a world model and plans using step-by-step virtual rollouts. This
type of world model quickly diverges from reality when the planning horizon
increases, thus struggling at long-horizon planning. How can we learn world
models that endow agents with the ability to do temporally extended reasoning?
In this work, we propose to learn graph-structured world models composed of
sparse, multi-step transitions. We devise a novel algorithm to learn latent
landmarks that are scattered (in terms of reachability) across the goal space
as the nodes on the graph. In this same graph, the edges are the reachability
estimates distilled from Q-functions. On a variety of high-dimensional
continuous control tasks ranging from robotic manipulation to navigation, we
demonstrate that our method, named L3P, significantly outperforms prior work,
and is oftentimes the only method capable of leveraging both the robustness of
model-free RL and generalization of graph-search algorithms. We believe our
work is an important step towards scalable planning in reinforcement learning.

该论文提出 L3P 算法，使用稀疏的多步转换学习基于图结构的世界模型并生成 Q - 函数，其在高维连续控制任务上具有优越的性能，是深度强化学习中可扩展规划的重要进展。

将世界模型视为图：学习用于规划的潜在地标

World Model as a Graph: Learning Latent Landmarks for Planning

Learning on 3D structures of large biomolecules is emerging as a distinct
area in machine learning, but there has yet to emerge a unifying network
architecture that simultaneously leverages the graph-structured and geometric
aspects of the problem domain. To address this gap, we introduce geometric
vector perceptrons, which extend standard dense layers to operate on
collections of Euclidean vectors. Graph neural networks equipped with such
layers are able to perform both geometric and relational reasoning on efficient
and natural representations of macromolecular structure. We demonstrate our
approach on two important problems in learning from protein structure: model
quality assessment and computational protein design. Our approach improves over
existing classes of architectures, including state-of-the-art graph-based and
voxel-based methods. We release our code at this https URL

通过引入基于几何向量感知器的图神经网络，在大型生物分子的 3D 结构上进行学习并利用其图结构和几何方面，从而在蛋白结构的质量评估和计算蛋白质设计两个重要问题上实现了优化，改进了现有各类体系结构，其中包括最先进的基于图的和基于体素的方法。