Theorem proving is a fundamental aspect of mathematics, spanning from
informal reasoning in mathematical language to rigorous derivations in formal
systems. In recent years, the advancement of deep learning, especially the
emergence of large language models, has sparked a notable surge of research
exploring these techniques to enhance the process of theorem proving. This
paper presents a pioneering comprehensive survey of deep learning for theorem
proving by offering i) a thorough review of existing approaches across various
tasks such as autoformalization, premise selection, proofstep generation, and
proof search; ii) a meticulous summary of available datasets and strategies for
data generation; iii) a detailed analysis of evaluation metrics and the
performance of state-of-the-art; and iv) a critical discussion on the
persistent challenges and the promising avenues for future exploration. Our
survey aims to serve as a foundational reference for deep learning approaches
in theorem proving, seeking to catalyze further research endeavors in this
rapidly growing field.

该论文提供了一项深度学习在定理证明中的全面调研，包括现有方法的综述、数据集和策略的详细总结、评估指标和最先进技术的性能分析，以及未来研究的挑战和发展方向的批判性讨论。该调研旨在成为深度学习在定理证明中的基础参考，促进这个迅速发展领域的进一步研究。

深度学习在定理证明中的调查

A Survey on Deep Learning for Theorem Proving

Graph neural networks (GNNs) are emerging for machine learning research on
graph-structured data. GNNs achieve state-of-the-art performance on many tasks,
but they face scalability challenges when it comes to real-world applications
that have numerous data and strict latency requirements. Many studies have been
conducted on how to accelerate GNNs in an effort to address these challenges.
These acceleration techniques touch on various aspects of the GNN pipeline,
from smart training and inference algorithms to efficient systems and
customized hardware. As the amount of research on GNN acceleration has grown
rapidly, there lacks a systematic treatment to provide a unified view and
address the complexity of relevant works. In this survey, we provide a taxonomy
of GNN acceleration, review the existing approaches, and suggest future
research directions. Our taxonomic treatment of GNN acceleration connects the
existing works and sets the stage for further development in this area.

本次调查报告提供了图神经网络加速的分类法，并回顾了现有方法，并提出了未来的研究方向，以便统一视图和解决相关工作的复杂性。

图神经网络加速综述：算法、系统和定制硬件

A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and  Customized Hardware

Animated avatars, which look and talk like humans, are iconic visions of the
future of AI-powered systems. Through many sci-fi movies we are acquainted with
the idea of speaking to such virtual personalities as if they were humans.
Today, we talk more and more to machines like Apple's Siri, e.g., to ask them
for the weather forecast. However, when asked for recommendations, e.g., for a
restaurant to go to, the limitations of such devices quickly become obvious.
They do not engage in a conversation to find out what we might prefer, they
often do not provide explanations for what they recommend, and they may have
difficulties remembering what was said one minute earlier. Conversational
recommender systems promise to address these limitations. In this paper, we
review existing approaches to build such systems, which developments we observe
today, which challenges are still open and why the development of
conversational recommenders represents one of the next grand challenges of AI.

这篇文章综述了建立对话推荐系统的现有方法、目前的发展及其面临的挑战，认为对话推荐系统是未来人工智能系统的重要发展方向之一。