Towards robust automated math problem solving: a survey of statistical and deep learning approaches

IF 2.3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolutionary Intelligence Pub Date : 2024-07-08 DOI:10.1007/s12065-024-00957-0
Amrutesh Saraf, P. Kamat, Shilpa Gite, Satish Kumar, K. Kotecha
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实现稳健的自动数学解题:统计和深度学习方法概览
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来源期刊
Evolutionary Intelligence
Evolutionary Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.80
自引率
0.00%
发文量
108
期刊介绍: This Journal provides an international forum for the timely publication and dissemination of foundational and applied research in the domain of Evolutionary Intelligence. The spectrum of emerging fields in contemporary artificial intelligence, including Big Data, Deep Learning, Computational Neuroscience bridged with evolutionary computing and other population-based search methods constitute the flag of Evolutionary Intelligence Journal.Topics of interest for Evolutionary Intelligence refer to different aspects of evolutionary models of computation empowered with intelligence-based approaches, including but not limited to architectures, model optimization and tuning, machine learning algorithms, life inspired adaptive algorithms, swarm-oriented strategies, high performance computing, massive data processing, with applications to domains like computer vision, image processing, simulation, robotics, computational finance, media, internet of things, medicine, bioinformatics, smart cities, and similar. Surveys outlining the state of art in specific subfields and applications are welcome.
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