This paper presents sandra, a neuro-symbolic reasoner combining vectorial
representations with deductive reasoning. Sandra builds a vector space
constrained by an ontology and performs reasoning over it. The geometric nature
of the reasoner allows its combination with neural networks, bridging the gap
with symbolic knowledge representations. Sandra is based on the Description and
Situation (DnS) ontology design pattern, a formalization of frame semantics.
Given a set of facts (a situation) it allows to infer all possible perspectives
(descriptions) that can provide a plausible interpretation for it, even in
presence of incomplete information. We prove that our method is correct with
respect to the DnS model. We experiment with two different tasks and their
standard benchmarks, demonstrating that, without increasing complexity, sandra
(i) outperforms all the baselines (ii) provides interpretability in the
classification process, and (iii) allows control over the vector space, which
is designed a priori.

这篇论文介绍了一种名为 sandra 的神经符号推理器，它将向量表示与演绎推理相结合。Sandra 利用本体论建立了一个向量空间，并对其进行推理。该推理器的几何性质使其能够与神经网络结合，弥合了符号知识表示与神经网络之间的差距。通过描述和情境 (DnS) 本体设计模式，Sandra 可以推断出所有可能的角度 (描述)，以合理解释给定的事实 (情境)，即使信息不完整。我们证明了我们的方法与 DnS 模型是正确的。我们在两个不同的任务和它们的标准基准上进行实验，证明 sandra 在不增加复杂性的情况下 (i) 胜过了所有基线 (ii) 在分类过程中提供了可解释性，以及 (iii) 可以控制预先设计好的向量空间。

Sandra—— 基于描述和情境的神经符号推理器

Sandra -- A Neuro-Symbolic Reasoner Based On Descriptions And Situations

Federated learning allows clients to collaboratively learn statistical models
while keeping their data local. Federated learning was originally used to train
a unique global model to be served to all clients, but this approach might be
sub-optimal when clients' local data distributions are heterogeneous. In order
to tackle this limitation, recent personalized federated learning methods train
a separate model for each client while still leveraging the knowledge available
at other clients. In this work, we exploit the ability of deep neural networks
to extract high quality vectorial representations (embeddings) from non-tabular
data, e.g., images and text, to propose a personalization mechanism based on
local memorization. Personalization is obtained by interpolating a collectively
trained global model with a local $k$-nearest neighbors (kNN) model based on
the shared representation provided by the global model. We provide
generalization bounds for the proposed approach in the case of binary
classification, and we show on a suite of federated datasets that this approach
achieves significantly higher accuracy and fairness than state-of-the-art
methods.

通过利用深度神经网络从非表格数据（如图像和文本）提取高质量特征向量来提出一种基于本地记忆的个性化机制，该机制与基于全局模型的交叉训练相结合，使用局部ｋ- 近邻模型实现个性化，并且在二元分类情况下给出了一般化边界。在一系列联合数据集上实验证明了这种方法的准确性和公平性显著优于现有的状态 - of-the-art 方法。

本地记忆化的个性化联邦学习

Personalized Federated Learning through Local Memorization

Transformer-based architectures have shown great success in image captioning,
where object regions are encoded and then attended into the vectorial
representations to guide the caption decoding. However, such vectorial
representations only contain region-level information without considering the
global information reflecting the entire image, which fails to expand the
capability of complex multi-modal reasoning in image captioning. In this paper,
we introduce a Global Enhanced Transformer (termed GET) to enable the
extraction of a more comprehensive global representation, and then adaptively
guide the decoder to generate high-quality captions. In GET, a Global Enhanced
Encoder is designed for the embedding of the global feature, and a Global
Adaptive Decoder are designed for the guidance of the caption generation. The
former models intra- and inter-layer global representation by taking advantage
of the proposed Global Enhanced Attention and a layer-wise fusion module. The
latter contains a Global Adaptive Controller that can adaptively fuse the
global information into the decoder to guide the caption generation. Extensive
experiments on MS COCO dataset demonstrate the superiority of our GET over many
state-of-the-arts.

该研究介绍了一种名为 GET 的全局增强变压器，能够提取更全面的全局表示，并自适应地引导解码器生成高质量的图像说明。