ACM Computing Surveys最新文献_第4页

Carbon-Efficient Software Design and Development: A Systematic Literature Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728638

Ornela Danushi, Stefano Forti, Jacopo Soldani

引用次数: 0

Ante-Hoc Methods for Interpretable Deep Models: A Survey 可解释深度模型的前置方法：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728637

Antonio Di Marino, Vincenzo Bevilacqua, Angelo Ciaramella, Ivanoe De Falco, Giovanna Sannino

引用次数: 0

Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728636

Gun Il Kim, Sunga Hwang, Beakcheol Jang

{"title":"Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review","authors":"Gun Il Kim, Sunga Hwang, Beakcheol Jang","doi":"10.1145/3728636","DOIUrl":"https://doi.org/10.1145/3728636","url":null,"abstract":"Efficient compression and tuning techniques have become indispensable in addressing the increasing computational and memory demands of large language models (LLMs). While these models have demonstrated exceptional performance across a wide range of natural language processing tasks, their growing size and resource requirements pose significant challenges to accessibility and sustainability. This survey systematically reviews state-of-the-art methods in model compression, including compression techniques such as knowledge distillation, low-rank approximation, parameter pruning, and quantization, as well as tuning techniques such as parameter-efficient fine-tuning and inference optimization. Compression techniques, though well-established in traditional deep learning, require updated methodologies tailored to the scale and dynamics of LLMs. Simultaneously, parameter-efficient fine-tuning, exemplified by techniques like Low-Rank Adaptation (LoRA) and query tuning, emerges as a promising solution for adapting models with minimal resource overhead. This study provides a detailed taxonomy of these methods, examining their practical applications, strengths, and limitations. Critical gaps are identified in scalability, and the integration of compression and tuning strategies, signaling the need for unified frameworks and hybrid approaches to maximize efficiency and performance. By addressing these challenges, this survey aims to guide researchers toward sustainable, efficient, and accessible LLM development, ensuring their broader applicability across diverse domains while mitigating resource constraints.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"4 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion-Based Visual Art Creation: A Survey and New Perspectives

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-05 DOI: 10.1145/3728459

Bingyuan Wang, Qifeng Chen, Zeyu Wang

引用次数: 0

Tree-based Models for Vertical Federated Learning: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-04 DOI: 10.1145/3728314

Bingchen Qian, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou

引用次数: 0

Unified Empirical Evaluation and Comparison of Session-based Recommendation Algorithms

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-04 DOI: 10.1145/3728358

Qingbo Zhang, Xiangmin Zhou, Xiuzhen Zhang, Xiaochun Yang, Bin Wang, Xun Yi

{"title":"Unified Empirical Evaluation and Comparison of Session-based Recommendation Algorithms","authors":"Qingbo Zhang, Xiangmin Zhou, Xiuzhen Zhang, Xiaochun Yang, Bin Wang, Xun Yi","doi":"10.1145/3728358","DOIUrl":"https://doi.org/10.1145/3728358","url":null,"abstract":"Recently, session-based recommendation systems (SBRSs) have become a highly explored area, and numerous methods have been proposed. The abundance of related work poses a challenge for newcomers in comprehending the current research landscape and burdens researchers during method validation. Offering a thorough research overview helps newcomers understand the current research. Additionally, comparing representative methods in a consistent environment allows researchers to streamline their workload by focusing on the top-performing methods. Existing theory-oriented review articles introduce the main techniques employed in SBRSs but lack a detailed exploration of their specific applications. The most recent neural method evaluated in existing experiment-driven review was published in 2019, and the latest state-of-the-art methods haven’t been included. To address these gaps, this paper offers a more thorough overview of SBRSs. Specifically, we first categorize and overview existing methods. Then, we introduce the main techniques and illustrate their applications. The performance of representative methods is validated under identical experimental conditions to ensure reliable comparative results. Our findings indicate that dataset characteristics significantly impact model performance, and attention mechanisms-based and gated neural networks (GNNs)-based models generally outperform others. Finally, we propose potential directions for future research in SBRSs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"183 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-03 DOI: 10.1145/3727633

Md Mehedi Hasan, Moloud Abdar, Abbas Khosravi, Uwe Aickelin, Pietro Lio, Ibrahim Hossain, Ashikur Rahman, Saeid Nahavandi

{"title":"Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing","authors":"Md Mehedi Hasan, Moloud Abdar, Abbas Khosravi, Uwe Aickelin, Pietro Lio, Ibrahim Hossain, Ashikur Rahman, Saeid Nahavandi","doi":"10.1145/3727633","DOIUrl":"https://doi.org/10.1145/3727633","url":null,"abstract":"Although neural networks (especially deep neural networks) have achieved better-than-human performance in many fields, their real-world deployment is still questionable due to the lack of awareness about the limitations in their knowledge. To incorporate such awareness in the machine learning model, prediction with reject option (also known as selective classification or classification with abstention) has been proposed in the literature. In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks. To the best of our knowledge, this is the first study focusing on this aspect of neural networks. Moreover, we discuss different novel loss functions related to the reject option and post-training processing (if any) of network output for generating suitable measurements for knowledge awareness of the model. Finally, we address the application of the rejection option in reducing the prediction time for real-time problems and present a comprehensive summary of the techniques related to the reject option in the context of a wide variety of neural networks. Our code is available on GitHub: https://github.com/MehediHasanTutul/Reject_option.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"62 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Primer on Pretrained Multilingual Language Models

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-01 DOI: 10.1145/3727339

Sumanth Doddapaneni, Gowtham Ramesh, Mitesh Khapra, Anoop Kunchukuttan, Pratyush Kumar

引用次数: 0

Trojan Attacks and Countermeasures on Deep Neural Networks from Life-Cycle Perspective: A Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-31 DOI: 10.1145/3727640

Lingxin Jin, Xiangyu Wen, Wei Jiang, Jinyu Zhan, Xingzhi Zhou

{"title":"Trojan Attacks and Countermeasures on Deep Neural Networks from Life-Cycle Perspective: A Review","authors":"Lingxin Jin, Xiangyu Wen, Wei Jiang, Jinyu Zhan, Xingzhi Zhou","doi":"10.1145/3727640","DOIUrl":"https://doi.org/10.1145/3727640","url":null,"abstract":"Deep Neural Networks (DNNs) have been widely deployed in security-critical artificial intelligence systems, such as autonomous driving and facial recognition systems. However, recent research has revealed their susceptibility to Trojan information maliciously injected by attackers. This vulnerability is caused, on the one hand, by the complex architecture and non-interpretability of DNNs. On the other hand, external open-source datasets, pre-trained models, and intelligent service platforms further exacerbate the threat of Trojan attacks. This article presents the first comprehensive survey of Trojan attacks against DNNs from a lifecycle perspective, including training, post-training, and inference (deployment) stages. Specifically, this article reformulates the relationships of Trojan attacks with poisoning attacks, adversarial example attacks, and bit-flip attacks. Then, research on Trojan attacks against newly emerged model architectures (e.g., vision transformers and spiking neural networks) and in other research fields is investigated. Moreover, this article also provides a comprehensive review of countermeasures (including detection and elimination) against Trojan attacks. Further, it evaluates the practical effectiveness of existing defense strategies against Trojan attacks at different lifecycle stages. Finally, we conclude the survey and provide constructive insights to advance research on Trojan attacks and countermeasures.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"12 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Review on Autonomous Navigation

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-31 DOI: 10.1145/3727642

Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, Shady Mohamed, Navid Mohajer, Mohammad Rokonuzzaman, Ibrahim Hossain

{"title":"A Comprehensive Review on Autonomous Navigation","authors":"Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, Shady Mohamed, Navid Mohajer, Mohammad Rokonuzzaman, Ibrahim Hossain","doi":"10.1145/3727642","DOIUrl":"https://doi.org/10.1145/3727642","url":null,"abstract":"The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"102 4 Pt 1 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0