Reconciling symbolic and distributed representations is a crucial challenge
that can potentially resolve the limitations of current deep learning.
Remarkable advances in this direction have been achieved recently via
generative object-centric representation models. While learning a recognition
model that infers object-centric symbolic representations like bounding boxes
from raw images in an unsupervised way, no such model can provide another
important ability of a generative model, i.e., generating (sampling) according
to the structure of learned world density. In this paper, we propose Generative
Neurosymbolic Machines, a generative model that combines the benefits of
distributed and symbolic representations to support both structured
representations of symbolic components and density-based generation. These two
crucial properties are achieved by a two-layer latent hierarchy with the global
distributed latent for flexible density modeling and the structured symbolic
latent map. To increase the model flexibility in this hierarchical structure,
we also propose the StructDRAW prior. In experiments, we show that the proposed
model significantly outperforms the previous structured representation models
as well as the state-of-the-art non-structured generative models in terms of
both structure accuracy and image generation quality. Our code, datasets, and
trained models are available at this https URL

本文提出了生成神经符号机，它是一种将分散和符号表示的优点相结合的生成模型，支持符号组件的结构化表示和基于密度的图像生成。该模型在结构精度和图像生成质量方面显著优于以前的结构表示模型以及现有的非结构生成模型。

生成神经符号机

Generative Neurosymbolic Machines

Label inventories for fine-grained entity typing have grown in size and
complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic
spaces offer a mathematically appealing approach for learning hierarchical
representations of symbolic data. However, it is not clear how to integrate
hyperbolic components into downstream tasks. This is the first work that
proposes a fully hyperbolic model for multi-class multi-label classification,
which performs all operations in hyperbolic space. We evaluate the proposed
model on two challenging datasets and compare to different baselines that
operate under Euclidean assumptions. Our hyperbolic model infers the latent
hierarchy from the class distribution, captures implicit hyponymic relations in
the inventory, and shows performance on par with state-of-the-art methods on
fine-grained classification with remarkable reduction of the parameter size. A
thorough analysis sheds light on the impact of each component in the final
prediction and showcases its ease of integration with Euclidean layers.

提出了一个全超伽马模型进行多类多标签分类，该模型可以从类别分布中推断出潜在的层次关系，捕获库存中的隐含下义关系，并显示出与最先进的方法相当的细粒度分类性能，具有可观的参数规模缩减。