A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Xiaofeng Wu, Alan Ritter, Wei Xu
This paper introduces a taxonomy of tabular input representations and table understanding tasks, highlighting critical gaps including retrieval-focused tasks, challenges with complex structures, and limited generalization.
Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.
This survey systematically defines Small Language Models (SLMs), provides a taxonomy of methods for their acquisition, application, enhancement, and reliability, and offers frameworks for their effective use in resource-constrained settings.
Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi, et al.
A comprehensive survey of autoencoders, providing a taxonomy based on structures and principles, reviewing applications across multiple domains, and outlining limitations and future directions.
Yijia Xiao, Wanjia Zhao, Junkai Zhang, et al.
This paper provides the first comprehensive overview of Protein LLMs, covering architectures, training datasets, evaluation metrics, and applications, with a structured taxonomy from over 100 articles.
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This paper provides a comprehensive taxonomy and review of graph neural networks, synthesizing 327 references to map advances and trends in the field.
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This paper categorizes self-supervised learning models into generative, contrastive, and hybrid approaches, providing a taxonomy and analysis of their underlying principles.
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This paper presents the first comprehensive survey of reward models in the LLM era, systematically reviewing related studies and introducing an elaborate taxonomy.
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This paper provides a comprehensive taxonomy and review of offline reinforcement learning methods, categorizing algorithms and discussing open problems in the field.
Yunkai Dang, Kaichen Huang, Jiahao Huo, et al.
A comprehensive survey of explainable and interpretable multimodal large language models, covering taxonomy, methods, benchmarks, and future directions.
Chen Shao, Elias Giacoumidis, Syed Moktacim Billah, et al.
This survey reviews machine learning applications in short-reach optical systems, introducing a taxonomy for time-series methods and addressing complexity challenges for practical deployment.
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This paper provides a comprehensive taxonomy, opportunities, and challenges of Representation Engineering (RepE) for large language models, a paradigm that steers model behavior by directly manipulating internal representations.
Jingkang Yang, Kaiyang Zhou, Yixuan Li, et al.
A comprehensive survey that unifies OOD detection, OOD localization, and OOD synthesis under a generalized framework, providing a taxonomy, benchmarks, and future directions.