Recent progress in sentence embedding, which represents the meaning of a
sentence as a point in a vector space, has achieved high performance on tasks
such as a semantic textual similarity (STS) task. However, sentence
representations as a point in a vector space can express only a part of the
diverse information that sentences have, such as asymmetrical relationships
between sentences. This paper proposes GaussCSE, a Gaussian distribution-based
contrastive learning framework for sentence embedding that can handle
asymmetric relationships between sentences, along with a similarity measure for
identifying inclusion relations. Our experiments show that GaussCSE achieves
the same performance as previous methods in natural language inference tasks,
and is able to estimate the direction of entailment relations, which is
difficult with point representations.

本文提出了一种基于高斯分布的对比学习框架 GaussCSE 用于句子嵌入，它可以处理句子间不对称的关系，同时还有一种相似度度量可以识别包含关系。实验证明 GaussCSE 在自然语言推断任务中与之前的方法一样表现出色，并能够估算蕴含关系的方向。

高斯嵌入表示的句子表征

Sentence Representations via Gaussian Embedding

We introduce a new problem of retrieving 3D models that are deformable to a
given query shape and present a novel deep deformation-aware embedding to solve
this retrieval task. 3D model retrieval is a fundamental operation for
recovering a clean and complete 3D model from a noisy and partial 3D scan.
However, given a finite collection of 3D shapes, even the closest model to a
query may not be satisfactory. This motivates us to apply 3D model deformation
techniques to adapt the retrieved model so as to better fit the query. Yet,
certain restrictions are enforced in most 3D deformation techniques to preserve
important features of the original model that prevent a perfect fitting of the
deformed model to the query. This gap between the deformed model and the query
induces asymmetric relationships among the models, which cannot be handled by
typical metric learning techniques. Thus, to retrieve the best models for
fitting, we propose a novel deep embedding approach that learns the asymmetric
relationships by leveraging location-dependent egocentric distance fields. We
also propose two strategies for training the embedding network. We demonstrate
that both of these approaches outperform other baselines in our experiments
with both synthetic and real data. Our project page can be found at
this https URL

本研究提出了一种基于深度学习的变形感知嵌入方法，致力于用于解决给定查询形状的可变形三维模型检索问题，并通过实验表明，该方法比传统基于度量的方法表现更出色。

变形感知三维模型嵌入及检索

Deformation-Aware 3D Model Embedding and Retrieval

Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product or cosine similarity, and enabling more expressive
parameterization of decision boundaries. This paper advocates for density-based
distributed embeddings and presents a method for learning representations in
the space of Gaussian distributions. We compare performance on various word
embedding benchmarks, investigate the ability of these embeddings to model
entailment and other asymmetric relationships, and explore novel properties of
the representation.

本文提倡使用基于密度的分布嵌入方法，并提出了一种在高斯分布空间中学习表示的方法，以更好地捕捉有关表示和其关系的不确定性，自然地表达不对称性，并使决策边界参数更有表达力。本文比较了各种单词嵌入基准测试的性能，调查了这些嵌入模型主导和其他不对称关系的能力，并探索了表示的新颖特性。