We explore end-to-end trained differentiable models that integrate natural
logic with neural networks, aiming to keep the backbone of natural language
reasoning based on the natural logic formalism while introducing subsymbolic
vector representations and neural components. The proposed model adapts module
networks to model natural logic operations, which is enhanced with a memory
component to model contextual information. Experiments show that the proposed
framework can effectively model monotonicity-based reasoning, compared to the
baseline neural network models without built-in inductive bias for
monotonicity-based reasoning. Our proposed model shows to be robust when
transferred from upward to downward inference. We perform further analyses on
the performance of the proposed model on aggregation, showing the effectiveness
of the proposed subcomponents on helping achieve better intermediate
aggregation performance.

我们提出了一种基于自然逻辑的神经网络模型，该模型使用模块网络模拟自然逻辑操作，并使用内存组件模拟上下文信息以有效建模单调性推理和聚合。实验表明，该模型可以有效地建模单调性推理，因此在聚合时得到更好的性能。

探索端到端可微的自然逻辑建模

Exploring End-to-End Differentiable Natural Logic Modeling

Existing methods for multi-domain image-to-image translation (or generation)
attempt to directly map an input image (or a random vector) to an image in one
of the output domains. However, most existing methods have limited scalability
and robustness, since they require building independent models for each pair of
domains in question. This leads to two significant shortcomings: (1) the need
to train exponential number of pairwise models, and (2) the inability to
leverage data from other domains when training a particular pairwise mapping.
Inspired by recent work on module networks, this paper proposes ModularGAN for
multi-domain image generation and image-to-image translation. ModularGAN
consists of several reusable and composable modules that carry on different
functions (e.g., encoding, decoding, transformations). These modules can be
trained simultaneously, leveraging data from all domains, and then combined to
construct specific GAN networks at test time, according to the specific image
translation task. This leads to ModularGAN's superior flexibility of generating
(or translating to) an image in any desired domain. Experimental results
demonstrate that our model not only presents compelling perceptual results but
also outperforms state-of-the-art methods on multi-domain facial attribute
transfer.

本文介绍了一种名为 ModularGAN 的多领域图像生成和图像到图像转换的方法，利用可复用和可组合的不同功能模块（例如编码、解码、变换）同时训练，并在测试时根据具体的图像转换任务组合这些模块，以显著提高生成（或转换为）任何所需领域图像的灵活性，并在多领域面部属性转移方面胜过现有方法。

模块化生成对抗网络

Modular Generative Adversarial Networks

Existing methods for visual reasoning attempt to directly map inputs to
outputs using black-box architectures without explicitly modeling the
underlying reasoning processes. As a result, these black-box models often learn
to exploit biases in the data rather than learning to perform visual reasoning.
Inspired by module networks, this paper proposes a model for visual reasoning
that consists of a program generator that constructs an explicit representation
of the reasoning process to be performed, and an execution engine that executes
the resulting program to produce an answer. Both the program generator and the
execution engine are implemented by neural networks, and are trained using a
combination of backpropagation and REINFORCE. Using the CLEVR benchmark for
visual reasoning, we show that our model significantly outperforms strong
baselines and generalizes better in a variety of settings.

本文采用模块化网络模型，使用神经网络实现程序生成器和执行引擎，通过反向传播和 REINFORCE 的训练方法，在视觉推理任务（CLEVR）上打破基线模型，表现显著优越，并在各种环境中具有更好的推广性能。