AI Open最新文献

英文中文

Proactive Recommendation in Social Networks: Steering user interest with causal inference 社交网络中的主动推荐：用因果推理引导用户兴趣

IF 14.8

AI Open

Pub Date : 2025-01-01 DOI: 10.1016/j.aiopen.2025.08.003

Hang Pan , Shuxian Bi , Wenjie Wang , Haoxuan Li , Peng Wu , Fuli Feng

Recommending items that solely cater to users’ historical interests narrows users’ horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct steering might not align perfectly with the evolution of users’ interests, detrimentally affecting the target users’ experience.

To avoid this issue, we propose a new task named Proactive Recommendation in Social Networks (PRSN) that indirectly steers users’ interest by utilizing the influence of social neighbors, i.e.,indirect steering by adjusting the exposure of a target item to target users’ neighbors. The key to PRSN lies in answering an interventional question: what would a target user’s feedback be on a target item if the item is exposed to the user’s different neighbors? To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item’s exposure to the user’s neighbors; and (2) adjusting the exposure of a target item to target users’ neighbors to trade-off steering performance and the damage to the neighbors’ experience. To this end, we propose a Neighbor Interference Recommendation (NIRec) framework with two modules: (1) an interference representation-based estimation module for modeling potential feedback; (2) a post-learning-based optimization module for adjusting a target item’s exposure to trade-off steering performance and the neighbors’ experience through greedy search. We conduct extensive semi-simulation experiments on real-world datasets, validating the steering effectiveness of NIRec. The code is available at https://github.com/HungPaan/NIRec.

只推荐迎合用户历史兴趣的商品会缩小用户的视野。最近的作品考虑通过直接调整暴露在目标用户面前的项目来引导目标用户超越他们的历史兴趣。然而，直接引导的推荐项目可能与用户兴趣的演变不完全一致，从而对目标用户的体验产生不利影响。为了避免这一问题，我们提出了一种新的任务，即主动推荐（PRSN），它通过利用社会邻居的影响间接引导用户的兴趣，即通过调整目标项目对目标用户邻居的曝光来间接引导。PRSN的关键在于回答一个干涉性问题：如果一个目标物品暴露给用户的不同邻居，目标用户对该物品的反馈会是什么？为了回答这个问题，我们采用因果推理，并将PRSN形式化为：(1)在网络干扰下，估计用户对物品的潜在反馈，该物品暴露于用户的邻居；(2)调整目标物品对目标用户邻居的暴露，以权衡转向性能和对邻居体验的损害。为此，我们提出了一个包含两个模块的邻居干扰推荐（NIRec）框架：(1)基于干扰表示的估计模块，用于建模潜在反馈；(2)基于后学习的优化模块，通过贪婪搜索调整目标项目对权衡转向性能的暴露和邻居的经验。我们在真实世界的数据集上进行了大量的半模拟实验，验证了NIRec的转向有效性。代码可在https://github.com/HungPaan/NIRec上获得。

{"title":"Proactive Recommendation in Social Networks: Steering user interest with causal inference","authors":"Hang Pan , Shuxian Bi , Wenjie Wang , Haoxuan Li , Peng Wu , Fuli Feng","doi":"10.1016/j.aiopen.2025.08.003","DOIUrl":"10.1016/j.aiopen.2025.08.003","url":null,"abstract":"<div><div>Recommending items that solely cater to users’ historical interests narrows users’ horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct steering might not align perfectly with the evolution of users’ interests, detrimentally affecting the target users’ experience.</div><div>To avoid this issue, we propose a new task named <em>Proactive Recommendation in Social Networks</em> (<strong>PRSN</strong>) that indirectly steers users’ interest by utilizing the influence of social neighbors, <em>i.e.,</em>indirect steering by adjusting the exposure of a target item to target users’ neighbors. The key to PRSN lies in answering an interventional question: <em>what would a target user’s feedback be on a target item if the item is exposed to the user’s different neighbors?</em> To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item’s exposure to the user’s neighbors; and (2) adjusting the exposure of a target item to target users’ neighbors to trade-off steering performance and the damage to the neighbors’ experience. To this end, we propose a <strong>N</strong>eighbor <strong>I</strong>nterference <strong>Rec</strong>ommendation (<strong>NIRec</strong>) framework with two modules: (1) an interference representation-based estimation module for modeling potential feedback; (2) a post-learning-based optimization module for adjusting a target item’s exposure to trade-off steering performance and the neighbors’ experience through greedy search. We conduct extensive semi-simulation experiments on real-world datasets, validating the steering effectiveness of NIRec. The code is available at <span><span>https://github.com/HungPaan/NIRec</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 130-141"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 14.8

AI Open

Pub Date : 2025-01-01

引用次数: 0

Multi-scale texture loss for CT denoising with GANs gan对CT去噪的多尺度纹理损失

IF 14.8

AI Open

Pub Date : 2025-01-01 DOI: 10.1016/j.aiopen.2025.09.001

Francesco Di Feola , Lorenzo Tronchin , Valerio Guarrasi , Paolo Soda

Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp highly complex and non-linear textural relationships in the training process, this work presents a novel approach to capture and embed multi-scale texture information into the loss function. Our method introduces a differentiable multi-scale texture representation of the images dynamically aggregated by a self-attention layer, thus exploiting end-to-end gradient-based optimization. We validate our approach by carrying out extensive experiments in the context of low-dose CT denoising, a challenging application that aims to enhance the quality of noisy CT scans. We utilize three publicly available datasets, including one simulated and two real datasets. The results are promising as compared to other well-established loss functions, being also consistent across three different GAN architectures. The code is available at: https://github.com/trainlab/MSTLF-TextureLoss.

生成对抗网络（GANs）已被证明是医学成像降噪应用的强大框架。然而，基于gan的去噪算法在捕获图像中的复杂关系方面仍然存在局限性。在这方面，损失函数在指导图像生成过程中起着至关重要的作用，它包含了合成图像与真实图像的差异。为了在训练过程中掌握高度复杂和非线性的纹理关系，本文提出了一种新的方法来捕获和嵌入多尺度纹理信息到损失函数中。我们的方法引入了由自关注层动态聚合的图像的可微多尺度纹理表示，从而利用了端到端基于梯度的优化。我们通过在低剂量CT去噪背景下进行广泛的实验来验证我们的方法，低剂量CT去噪是一项具有挑战性的应用，旨在提高噪声CT扫描的质量。我们使用三个公开可用的数据集，包括一个模拟数据集和两个真实数据集。与其他已建立的损失函数相比，结果是有希望的，并且在三种不同的GAN架构中也是一致的。代码可从https://github.com/trainlab/MSTLF-TextureLoss获得。

{"title":"Multi-scale texture loss for CT denoising with GANs","authors":"Francesco Di Feola , Lorenzo Tronchin , Valerio Guarrasi , Paolo Soda","doi":"10.1016/j.aiopen.2025.09.001","DOIUrl":"10.1016/j.aiopen.2025.09.001","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp highly complex and non-linear textural relationships in the training process, this work presents a novel approach to capture and embed multi-scale texture information into the loss function. Our method introduces a differentiable multi-scale texture representation of the images dynamically aggregated by a self-attention layer, thus exploiting end-to-end gradient-based optimization. We validate our approach by carrying out extensive experiments in the context of low-dose CT denoising, a challenging application that aims to enhance the quality of noisy CT scans. We utilize three publicly available datasets, including one simulated and two real datasets. The results are promising as compared to other well-established loss functions, being also consistent across three different GAN architectures. The code is available at: <span><span>https://github.com/trainlab/MSTLF-TextureLoss</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 142-154"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChatLLM network: More brains, more intelligence ChatLLM网络：更多的大脑，更多的智慧

AI Open

Pub Date : 2025-01-01 DOI: 10.1016/j.aiopen.2025.01.001

Rui Hao , Linmei Hu , Weijian Qi , Qingliu Wu , Yirui Zhang , Liqiang Nie

Dialogue-based language models mark a huge milestone in the field of artificial intelligence, by their impressive ability to interact with users, as well as a series of challenging tasks prompted by customized instructions. However, the prevalent large-scale dialogue-based language models like ChatGPT still have room for improvement, such as unstable responses to questions and the inability to think cooperatively like humans. Considering the ability of dialogue-based language models in conversation and their inherent randomness in thinking, we propose ChatLLM network that allows multiple dialogue-based language models to interact, provide feedback, and think together. We design a network of ChatLLMs, consisting multiple layers of language models. Specifically, individual instances of language model may possess distinct perspectives towards the same problem, and by consolidating these diverse viewpoints via a separate language model, the ChatLLM network system can conduct decision-making more objectively and comprehensively. In addition, a language-based feedback mechanism comparable to backpropagation is devised to update the outputs of the language models within the network. This stratified system of interaction can be analogized to the relationship between leaders and employees in a social organization, where collective decision-making often yields superior judgments or resolutions. Experiments on datasets demonstrate that our network attains significant improvements in problem-solving, leading to observable progress amongst each member.

基于对话的语言模型在人工智能领域是一个巨大的里程碑，它们与用户互动的能力令人印象深刻，还能根据定制指令完成一系列具有挑战性的任务。然而，目前流行的大型对话式语言模型（如 ChatGPT）仍有改进的余地，如对问题的回答不稳定，无法像人类一样合作思考等。考虑到基于对话的语言模型在对话中的能力及其固有的思维随机性，我们提出了 ChatLLM 网络，它允许多个基于对话的语言模型进行互动、反馈和共同思考。我们设计了一个由多层语言模型组成的 ChatLLM 网络。具体来说，语言模型的各个实例可能对同一问题持有不同的观点，而通过单独的语言模型整合这些不同的观点，ChatLLM 网络系统可以更客观、更全面地进行决策。此外，还设计了一种类似于反向传播的基于语言的反馈机制，用于更新网络内语言模型的输出。这种分层互动系统可以类比为社会组织中领导与员工之间的关系，在这种关系中，集体决策往往会产生更优越的判断或解决方案。数据集上的实验表明，我们的网络在解决问题方面取得了显著的进步，每个成员都取得了可观的进步。

{"title":"ChatLLM network: More brains, more intelligence","authors":"Rui Hao , Linmei Hu , Weijian Qi , Qingliu Wu , Yirui Zhang , Liqiang Nie","doi":"10.1016/j.aiopen.2025.01.001","DOIUrl":"10.1016/j.aiopen.2025.01.001","url":null,"abstract":"<div><div>Dialogue-based language models mark a huge milestone in the field of artificial intelligence, by their impressive ability to interact with users, as well as a series of challenging tasks prompted by customized instructions. However, the prevalent large-scale dialogue-based language models like ChatGPT still have room for improvement, such as unstable responses to questions and the inability to think cooperatively like humans. Considering the ability of dialogue-based language models in conversation and their inherent randomness in thinking, we propose ChatLLM network that allows multiple dialogue-based language models to interact, provide feedback, and think together. We design a network of ChatLLMs, consisting multiple layers of language models. Specifically, individual instances of language model may possess distinct perspectives towards the same problem, and by consolidating these diverse viewpoints via a separate language model, the ChatLLM network system can conduct decision-making more objectively and comprehensively. In addition, a language-based feedback mechanism comparable to backpropagation is devised to update the outputs of the language models within the network. This stratified system of interaction can be analogized to the relationship between leaders and employees in a social organization, where collective decision-making often yields superior judgments or resolutions. Experiments on datasets demonstrate that our network attains significant improvements in problem-solving, leading to observable progress amongst each member.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 45-52"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum regarding Declaration of Competing Interest statements in previously published articles 关于先前发表的文章中竞争利益声明的勘误表

IF 14.8

AI Open

Pub Date : 2025-01-01 DOI: 10.1016/j.aiopen.2024.01.002

引用次数: 0

IF 14.8

AI Open

Pub Date : 2025-01-01

引用次数: 0

IF 14.8

AI Open

Pub Date : 2025-01-01

引用次数: 0

Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks 解谜：增强深度网络解释的忠实性和可理解性

AI Open

Pub Date : 2025-01-01 DOI: 10.1016/j.aiopen.2025.02.001

Michail Mamalakis , Antonios Mamalakis , Ingrid Agartz , Lynn Egeland Mørch-Johnsen , Graham K. Murray , John Suckling , Pietro Lio

The accelerated progress of artificial intelligence (AI) has popularized deep learning models across various domains, yet their inherent opacity poses challenges, particularly in critical fields like healthcare, medicine, and the geosciences. Explainable AI (XAI) has emerged to shed light on these ’black box’ models, aiding in deciphering their decision-making processes. However, different XAI methods often produce significantly different explanations, leading to high inter-method variability that increases uncertainty and undermines trust in deep networks’ predictions. In this study, we address this challenge by introducing a novel framework designed to enhance the explainability of deep networks through a dual focus on maximizing both accuracy and comprehensibility in the explanations. Our framework integrates outputs from multiple established XAI methods and leverages a non-linear neural network model, termed the ‘Explanation optimizer,’ to construct a unified, optimal explanation. The optimizer uses two primary metrics — faithfulness and complexity — to evaluate the quality of the explanations. Faithfulness measures the accuracy with which the explanation reflects the network’s decision-making, while complexity assesses the comprehensibility of the explanation. By balancing these metrics, the optimizer provides explanations that are both accurate and accessible, addressing a central limitation in current XAI methods. Through experiments on multi-class and binary classification tasks in both 2D object and 3D neuroscience imaging, we validate the efficacy of our approach. Our explanation optimizer achieved superior faithfulness scores, averaging 155% and 63% higher than the best-performing individual XAI methods in the 3D and 2D applications, respectively, while also reducing complexity to enhance comprehensibility. These results demonstrate that optimal explanations based on specific quality criteria are achievable, offering a solution to the issue of inter-method variability in the current XAI literature and supporting more trustworthy deep network predictions.

人工智能（AI）的加速发展使深度学习模型在各个领域得到普及，但其固有的不透明性带来了挑战，特别是在医疗保健、医学和地球科学等关键领域。可解释人工智能（XAI）的出现，揭示了这些“黑匣子”模型，帮助破译它们的决策过程。然而，不同的XAI方法通常会产生显著不同的解释，导致方法间的高可变性，增加了不确定性，破坏了对深度网络预测的信任。在本研究中，我们通过引入一个新的框架来解决这一挑战，该框架旨在通过双重关注最大化解释的准确性和可理解性来增强深度网络的可解释性。我们的框架集成了多个已建立的XAI方法的输出，并利用称为“解释优化器”的非线性神经网络模型来构建统一的最佳解释。优化器使用两个主要指标——忠实度和复杂性——来评估解释的质量。信度衡量的是解释反映网络决策的准确性，而复杂性评估的是解释的可理解性。通过平衡这些指标，优化器提供了既准确又可访问的解释，解决了当前XAI方法中的一个主要限制。通过对二维目标和三维神经科学成像的多类和二元分类任务进行实验，验证了该方法的有效性。我们的解释优化器获得了卓越的忠实度分数，在3D和2D应用中，平均比表现最好的单个XAI方法分别高出155%和63%，同时还降低了复杂性以提高可理解性。这些结果表明，基于特定质量标准的最佳解释是可以实现的，为当前XAI文献中的方法间变异性问题提供了解决方案，并支持更可信的深度网络预测。

{"title":"Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks","authors":"Michail Mamalakis , Antonios Mamalakis , Ingrid Agartz , Lynn Egeland Mørch-Johnsen , Graham K. Murray , John Suckling , Pietro Lio","doi":"10.1016/j.aiopen.2025.02.001","DOIUrl":"10.1016/j.aiopen.2025.02.001","url":null,"abstract":"<div><div>The accelerated progress of artificial intelligence (AI) has popularized deep learning models across various domains, yet their inherent opacity poses challenges, particularly in critical fields like healthcare, medicine, and the geosciences. Explainable AI (XAI) has emerged to shed light on these ’black box’ models, aiding in deciphering their decision-making processes. However, different XAI methods often produce significantly different explanations, leading to high inter-method variability that increases uncertainty and undermines trust in deep networks’ predictions. In this study, we address this challenge by introducing a novel framework designed to enhance the explainability of deep networks through a dual focus on maximizing both accuracy and comprehensibility in the explanations. Our framework integrates outputs from multiple established XAI methods and leverages a non-linear neural network model, termed the ‘Explanation optimizer,’ to construct a unified, optimal explanation. The optimizer uses two primary metrics — faithfulness and complexity — to evaluate the quality of the explanations. Faithfulness measures the accuracy with which the explanation reflects the network’s decision-making, while complexity assesses the comprehensibility of the explanation. By balancing these metrics, the optimizer provides explanations that are both accurate and accessible, addressing a central limitation in current XAI methods. Through experiments on multi-class and binary classification tasks in both 2D object and 3D neuroscience imaging, we validate the efficacy of our approach. Our explanation optimizer achieved superior faithfulness scores, averaging 155% and 63% higher than the best-performing individual XAI methods in the 3D and 2D applications, respectively, while also reducing complexity to enhance comprehensibility. These results demonstrate that optimal explanations based on specific quality criteria are achievable, offering a solution to the issue of inter-method variability in the current XAI literature and supporting more trustworthy deep network predictions.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 70-81"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable graph attention-based instance selection via mini-batch sampling and hierarchical hashing 通过小批量采样和分层哈希进行可扩展的基于关注的图实例选择

IF 14.8

AI Open

Pub Date : 2025-01-01 DOI: 10.1016/j.aiopen.2025.08.004

Zahiriddin Rustamov , Ayham Zaitouny , Nazar Zaki

Instance selection (IS) addresses the critical challenge of reducing dataset size while keeping informative characteristics, becoming increasingly important as datasets grow to millions of instances. Current IS methods often struggle with capturing complex relationships in high-dimensional spaces and scale with large datasets. This paper introduces a graph attention-based instance selection (GAIS) method that uses attention mechanisms to identify informative instances through their structural relationships in graph representations. We present two approaches for scalable graph construction: a distance-based mini-batch sampling technique that achieves dataset-size-independent complexity through strategic batch processing, and a hierarchical hashing approach that enables efficient similarity computation through random projections. The mini-batch approach keeps class distributions through stratified sampling, while the hierarchical hashing method captures relationships at multiple granularities through single-level, multi-level, and multi-view variants. Experiments across 39 datasets show that GAIS achieves reduction rates above 96% while maintaining or improving model performance relative to state-of-the-art IS methods. The findings show that the distance-based mini-batch approach offers an optimal efficiency for large-scale datasets, while multi-view variants excel on complex, high-dimensional data, demonstrating that attention-based importance scoring can effectively identify instances important for maintaining decision boundaries while avoiding computationally prohibitive pairwise comparisons. The code is publicly available at https://github.com/zahiriddin-rustamov/gais.

实例选择（IS）解决了在保持信息特征的同时减少数据集大小的关键挑战，随着数据集增长到数百万个实例，它变得越来越重要。当前的IS方法通常难以在高维空间中捕获复杂的关系，并且难以在大型数据集上进行扩展。本文介绍了一种基于图注意的实例选择（GAIS）方法，该方法利用注意机制通过图表示中的结构关系来识别信息实例。我们提出了两种可扩展图构建方法：一种基于距离的小批量采样技术，通过战略性批处理实现与数据集大小无关的复杂性，以及一种分层哈希方法，通过随机投影实现高效的相似性计算。小批处理方法通过分层抽样保持类分布，而分层散列方法通过单级、多级和多视图变体捕获多粒度的关系。39个数据集的实验表明，相对于最先进的IS方法，GAIS在保持或提高模型性能的同时，实现了96%以上的降噪率。研究结果表明，基于距离的小批量方法为大规模数据集提供了最佳效率，而多视图变体在复杂的高维数据上表现出色，这表明基于注意力的重要性评分可以有效地识别对维持决策边界重要的实例，同时避免了计算上的两两比较。该代码可在https://github.com/zahiriddin-rustamov/gais上公开获得。

{"title":"Scalable graph attention-based instance selection via mini-batch sampling and hierarchical hashing","authors":"Zahiriddin Rustamov , Ayham Zaitouny , Nazar Zaki","doi":"10.1016/j.aiopen.2025.08.004","DOIUrl":"10.1016/j.aiopen.2025.08.004","url":null,"abstract":"<div><div>Instance selection (IS) addresses the critical challenge of reducing dataset size while keeping informative characteristics, becoming increasingly important as datasets grow to millions of instances. Current IS methods often struggle with capturing complex relationships in high-dimensional spaces and scale with large datasets. This paper introduces a graph attention-based instance selection (GAIS) method that uses attention mechanisms to identify informative instances through their structural relationships in graph representations. We present two approaches for scalable graph construction: a distance-based mini-batch sampling technique that achieves dataset-size-independent complexity through strategic batch processing, and a hierarchical hashing approach that enables efficient similarity computation through random projections. The mini-batch approach keeps class distributions through stratified sampling, while the hierarchical hashing method captures relationships at multiple granularities through single-level, multi-level, and multi-view variants. Experiments across 39 datasets show that GAIS achieves reduction rates above 96% while maintaining or improving model performance relative to state-of-the-art IS methods. The findings show that the distance-based mini-batch approach offers an optimal efficiency for large-scale datasets, while multi-view variants excel on complex, high-dimensional data, demonstrating that attention-based importance scoring can effectively identify instances important for maintaining decision boundaries while avoiding computationally prohibitive pairwise comparisons. The code is publicly available at <span><span>https://github.com/zahiriddin-rustamov/gais</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 167-182"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 14.8

AI Open

Pub Date : 2025-01-01

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

AI Open

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀