There has been a growing interest in recent years in modelling multiple
modalities (or views) of data to for example, understand the relationship
between modalities or to generate missing data. Multi-view autoencoders have
gained significant traction for their adaptability and versatility in modelling
multi-modal data, demonstrating an ability to tailor their approach to suit the
characteristics of the data at hand. However, most multi-view autoencoders have
inconsistent notation and are often implemented using different coding
frameworks. To address this, we present a unified mathematical framework for
multi-view autoencoders, consolidating their formulations. Moreover, we offer
insights into the motivation and theoretical advantages of each model. To
facilitate accessibility and practical use, we extend the documentation and
functionality of the previously introduced \texttt{multi-view-AE} library. This
library offers Python implementations of numerous multi-view autoencoder
models, presented within a user-friendly framework. Through benchmarking
experiments, we evaluate our implementations against previous ones,
demonstrating comparable or superior performance. This work aims to establish a
cohesive foundation for multi-modal modelling, serving as a valuable
educational resource in the field.

本篇论文针对多模态建模提出了一个统一的数学框架，同时扩展了	exttt {multi-view-AE} 库的文档和功能，通过基准实验评估实现的性能，并作为该领域的教育资源，旨在建立多模态建模的一致基础。

多视图自编码器教程

A tutorial on multi-view autoencoders using the multi-view-AE library

Offline reinforcement learning (RL) is a promising direction that allows RL
agents to pre-train on large datasets, avoiding the recurrence of expensive
data collection. To advance the field, it is crucial to generate large-scale
datasets. Compositional RL is particularly appealing for generating such large
datasets, since 1) it permits creating many tasks from few components, 2) the
task structure may enable trained agents to solve new tasks by combining
relevant learned components, and 3) the compositional dimensions provide a
notion of task relatedness. This paper provides four offline RL datasets for
simulated robotic manipulation created using the 256 tasks from CompoSuite
[Mendez et al., 2022a]. Each dataset is collected from an agent with a
different degree of performance, and consists of 256 million transitions. We
provide training and evaluation settings for assessing an agent's ability to
learn compositional task policies. Our benchmarking experiments on each setting
show that current offline RL methods can learn the training tasks to some
extent and that compositional methods significantly outperform
non-compositional methods. However, current methods are still unable to extract
the tasks' compositional structure to generalize to unseen tasks, showing a
need for further research in offline compositional RL.

本研究提供了四个来自 CompoSuite 的离线强化学习数据集，用于解决机器人操作的组合任务，评估表明组合方法比非组合方法优越，但当前方法仍无法提取任务的组合结构以推广到看不见的任务，需要进一步研究。

用于离线组合强化学习的机器人操作数据集

Robotic Manipulation Datasets for Offline Compositional Reinforcement  Learning

METHODS: First, a set of evaluation criteria is designed based on a
comprehensive literature review. Second, existing candidate criteria are
optimized for using a Delphi method by five experts in medicine and
engineering. Third, three clinical experts design a set of medical datasets to
interact with LLMs. Finally, benchmarking experiments are conducted on the
datasets. The responses generated by chatbots based on LLMs are recorded for
blind evaluations by five licensed medical experts. RESULTS: The obtained
evaluation criteria cover medical professional capabilities, social
comprehensive capabilities, contextual capabilities, and computational
robustness, with sixteen detailed indicators. The medical datasets include
twenty-seven medical dialogues and seven case reports in Chinese. Three
chatbots are evaluated, ChatGPT by OpenAI, ERNIE Bot by Baidu Inc., and Doctor
PuJiang (Dr. PJ) by Shanghai Artificial Intelligence Laboratory. Experimental
results show that Dr. PJ outperforms ChatGPT and ERNIE Bot in both
multiple-turn medical dialogue and case report scenarios.

通过对 LLMs 进行基于交互式医疗对话的实验评估，设计了一套涵盖医疗专业能力、社会综合能力、语境能力和计算机稳健性等方面的 16 个指标的评价标准，并针对这些标准选取了 ChatGPT, ERNIE Bot 和 Doctor PuJiang 三个聊天机器人进行了盲测试比较，其中 Doctor PuJiang 在多回合医疗对话和实证报告情景下表现最优。