计算机科学最新文献_第2页

Survey of real-time brainmedia in artistic exploration. 艺术探索中的实时脑媒体调查。

IF 3.2 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2024-11-18 DOI: 10.1186/s42492-024-00179-2

Rem RunGu Lin, Kang Zhang

引用次数: 0

Computing With Residue Numbers in High-Dimensional Representation. 用高维表示法计算残差数

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-11-18 DOI: 10.1162/neco_a_01723

Christopher J Kymn, Denis Kleyko, E Paxon Frady, Connor Bybee, Pentti Kanerva, Friedrich T Sommer, Bruno A Olshausen

{"title":"Computing With Residue Numbers in High-Dimensional Representation.","authors":"Christopher J Kymn, Denis Kleyko, E Paxon Frady, Connor Bybee, Pentti Kanerva, Friedrich T Sommer, Bruno A Olshausen","doi":"10.1162/neco_a_01723","DOIUrl":"https://doi.org/10.1162/neco_a_01723","url":null,"abstract":"We introduce residue hyperdimensional computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show how residue numbers can be represented as high-dimensional vectors in a manner that allows algebraic operations to be performed with component-wise, parallelizable operations on the vector elements. The resulting framework, when combined with an efficient method for factorizing high-dimensional vectors, can represent and operate on numerical values over a large dynamic range using resources that scale only logarithmically with the range, a vast improvement over previous methods. It also exhibits impressive robustness to noise. We demonstrate the potential for this framework to solve computationally difficult problems in visual perception and combinatorial optimization, showing improvement over baseline methods. More broadly, the framework provides a possible account for the computational operations of grid cells in the brain, and it suggests new machine learning architectures for representing and manipulating numerical data.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-37"},"PeriodicalIF":2.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Clinical large language models with misplaced focus 重点错位的临床大型语言模型

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-11-18 DOI: 10.1038/s42256-024-00929-0

Zining Luo, Haowei Ma, Zhiwu Li, Yuquan Chen, Yixin Sun, Aimin Hu, Jiang Yu, Yang Qiao, Junxian Gu, Hongying Li, Xuxi Peng, Dunrui Wang, Ying Liu, Zhenglong Liu, Jiebin Xie, Zhen Jiang, Gang Tian

{"title":"Clinical large language models with misplaced focus","authors":"Zining Luo, Haowei Ma, Zhiwu Li, Yuquan Chen, Yixin Sun, Aimin Hu, Jiang Yu, Yang Qiao, Junxian Gu, Hongying Li, Xuxi Peng, Dunrui Wang, Ying Liu, Zhenglong Liu, Jiebin Xie, Zhen Jiang, Gang Tian","doi":"10.1038/s42256-024-00929-0","DOIUrl":"https://doi.org/10.1038/s42256-024-00929-0","url":null,"abstract":"On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving results that sometimes exceed human performance in specific tasks. Although such success is encouraging, the development of the models used for these tasks may not align with the characteristics and needs of clinical practice.LLMs can be categorized as either open-source or closed-source. Open-source models, such as Meta’s Llama, allow developers to access source code, training data and documentation freely. By contrast, closed-source models are accessed only through official channels or application programming interfaces (APIs). Initially, open-source models dominated the LLM landscape, until the release of OpenAI’s GPT-3 in 20201, which attracted considerable commercial interest and shifted focus towards closed-source approaches2.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"18 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670260","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

Pri-DDQN: learning adaptive traffic signal control strategy through a hybrid agent Pri-DDQN：通过混合代理学习自适应交通信号控制策略

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-11-18 DOI: 10.1007/s40747-024-01651-5

Yanliu Zheng, Juan Luo, Han Gao, Yi Zhou, Keqin Li

{"title":"Pri-DDQN: learning adaptive traffic signal control strategy through a hybrid agent","authors":"Yanliu Zheng, Juan Luo, Han Gao, Yi Zhou, Keqin Li","doi":"10.1007/s40747-024-01651-5","DOIUrl":"https://doi.org/10.1007/s40747-024-01651-5","url":null,"abstract":"Adaptive traffic signal control is the core of the intelligent transportation system (ITS), which can effectively reduce the pressure on traffic congestion and improve travel efficiency. Methods based on deep Q-leaning network (DQN) have become the mainstream to solve single-intersection traffic signal control. However, most of them neglect the important difference of samples and the dependence of traffic states, and cannot quickly respond to randomly changing traffic flows. In this paper, we propose a new single-intersection traffic signal control method (Pri-DDQN) based on reinforcement learning and model the traffic environment as a reinforcement learning environment, and the agent chooses the best action to schedule the traffic flow at the intersection based on the real-time traffic states. With the goal of minimizing the waiting time and queue length at intersections, we use double DQN to train the agent, incorporate traffic state and reward into the loss function, and update the target network parameters asynchronously, to improve the agent’s learning ability. We try to use the power function to dynamically change the exploration rate to accelerate convergence. In addition, we introduce a priority-based dynamic experience replay mechanism to increase the sampling rate of important samples. The results show that Pri-DDQN achieves better performance, compared to the best baseline, it reduces the average queue length is reduced by 13.41%, and the average waiting time by 32.33% at the intersection.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"64 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Selective Inference for Change Point Detection by Recurrent Neural Network. 利用递归神经网络进行变化点检测的选择性推理

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-11-18 DOI: 10.1162/neco_a_01724

Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi

引用次数: 0

Warping the Residuals for Image Editing with StyleGAN 使用 StyleGAN 对残差进行翘曲处理以进行图像编辑

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2024-11-18 DOI: 10.1007/s11263-024-02301-6

Ahmet Burak Yildirim, Hamza Pehlivan, Aysegul Dundar

{"title":"Warping the Residuals for Image Editing with StyleGAN","authors":"Ahmet Burak Yildirim, Hamza Pehlivan, Aysegul Dundar","doi":"10.1007/s11263-024-02301-6","DOIUrl":"https://doi.org/10.1007/s11263-024-02301-6","url":null,"abstract":"StyleGAN models show editing capabilities via their semantically interpretable latent organizations which require successful GAN inversion methods to edit real images. Many works have been proposed for inverting images into StyleGAN’s latent space. However, their results either suffer from low fidelity to the input image or poor editing qualities, especially for edits that require large transformations. That is because low bit rate latent spaces lose many image details due to the information bottleneck even though it provides an editable space. On the other hand, higher bit rate latent spaces can pass all the image details to StyleGAN for perfect reconstruction of images but suffer from low editing qualities. In this work, we present a novel image inversion architecture that extracts high-rate latent features and includes a flow estimation module to warp these features to adapt them to edits. This is because edits often involve spatial changes in the image, such as adjustments to pose or smile. Thus, high-rate latent features must be accurately repositioned to match their new locations in the edited image space. We achieve this by employing flow estimation to determine the necessary spatial adjustments, followed by warping the features to align them correctly in the edited image. Specifically, we estimate the flows from StyleGAN features of edited and unedited latent codes. By estimating the high-rate features and warping them for edits, we achieve both high-fidelity to the input image and high-quality edits. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements.","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"64 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Generalization Analysis of Transformers in Distribution Regression. 配电回归中变压器的归纳分析。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-11-18 DOI: 10.1162/neco_a_01726

Peilin Liu, Ding-Xuan Zho

{"title":"Generalization Analysis of Transformers in Distribution Regression.","authors":"Peilin Liu, Ding-Xuan Zho","doi":"10.1162/neco_a_01726","DOIUrl":"https://doi.org/10.1162/neco_a_01726","url":null,"abstract":"In recent years, models based on the transformer architecture have seen widespread applications and have become one of the core tools in the field of deep learning. Numerous successful and efficient techniques, such as parameter-efficient fine-tuning and efficient scaling, have been proposed surrounding their applications to further enhance performance. However, the success of these strategies has always lacked the support of rigorous mathematical theory. To study the underlying mechanisms behind transformers and related techniques, we first propose a transformer learning framework motivated by distribution regression, with distributions being inputs, connect a two-stage sampling process with natural language processing, and present a mathematical formulation of the attention mechanism called attention operator. We demonstrate that by the attention operator, transformers can compress distributions into function representations without loss of information. Moreover, with the advantages of our novel attention operator, transformers exhibit a stronger capability to learn functionals with more complex structures than convolutional neural networks and fully connected networks. Finally, we obtain a generalization bound within the distribution regression framework. Throughout theoretical results, we further discuss some successful techniques emerging with large language models (LLMs), such as prompt tuning, parameter-efficient fine-tuning, and efficient scaling. We also provide theoretical insights behind these techniques within our novel analysis framework.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-34"},"PeriodicalIF":2.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Generalization Guarantees of Gradient Descent for Shallow Neural Networks. 浅层神经网络梯度下降的泛化保证

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-11-18 DOI: 10.1162/neco_a_01725

Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou

{"title":"Generalization Guarantees of Gradient Descent for Shallow Neural Networks.","authors":"Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou","doi":"10.1162/neco_a_01725","DOIUrl":"https://doi.org/10.1162/neco_a_01725","url":null,"abstract":"Significant progress has been made recently in understanding the generalization of neural networks (NNs) trained by gradient descent (GD) using the algorithmic stability approach. However, most of the existing research has focused on one-hidden-layer NNs and has not addressed the impact of different network scaling. Here, network scaling corresponds to the normalization of the layers. In this article, we greatly extend the previous work (Lei et al., 2022; Richards & Kuzborskij, 2021) by conducting a comprehensive stability and generalization analysis of GD for two-layer and three-layer NNs. For two-layer NNs, our results are established under general network scaling, relaxing previous conditions. In the case of three-layer NNs, our technical contribution lies in demonstrating its nearly co-coercive property by utilizing a novel induction strategy that thoroughly explores the effects of overparameterization. As a direct application of our general findings, we derive the excess risk rate of O(1/n) for GD in both two-layer and three-layer NNs. This sheds light on sufficient or necessary conditions for underparameterized and overparameterized NNs trained by GD to attain the desired risk rate of O(1/n). Moreover, we demonstrate that as the scaling factor increases or the network complexity decreases, less overparameterization is required for GD to achieve the desired error rates. Additionally, under a low-noise condition, we obtain a fast risk rate of O(1/n) for GD in both two-layer and three-layer NNs.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-59"},"PeriodicalIF":2.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

K-Means Centroids Initialization Based on Differentiation Between Instances Attributes 基于实例属性差异的 K-Means 中心点初始化

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2024-11-18 DOI: 10.1155/2024/7086878

Ali Akbar Khan, Muhammad Salman Bashir, Asma Batool, Muhammad Summair Raza, Muhammad Adnan Bashir

{"title":"K-Means Centroids Initialization Based on Differentiation Between Instances Attributes","authors":"Ali Akbar Khan, Muhammad Salman Bashir, Asma Batool, Muhammad Summair Raza, Muhammad Adnan Bashir","doi":"10.1155/2024/7086878","DOIUrl":"https://doi.org/10.1155/2024/7086878","url":null,"abstract":"<div>\u0000 The conventional K-Means clustering algorithm is widely used for grouping similar data points by initially selecting random centroids. However, the accuracy of clustering results is significantly influenced by the initial centroid selection. Despite different approaches, including various K-Means versions, suboptimal outcomes persist due to inadequate initial centroid choices and reliance on common normalization techniques like min-max normalization. In this study, we propose an improved algorithm that selects initial centroids more effectively by utilizing a novel formula to differentiate between instance attributes, creating a single weight for differentiation. We introduce a preprocessing phase for dataset normalization without forcing values into a specific range, yielding significantly improved results compared to unnormalized datasets and those normalized using min-max techniques. For our experiments, we used five real datasets and five simulated datasets. The proposed algorithm is evaluated using various metrics and an external benchmark measure, such as the Adjusted Rand Index (ARI), and compared with the traditional K-Means algorithm and 11 other modified K-Means algorithms. Experimental evaluations on these datasets demonstrate the superiority of our proposed methodologies, achieving an impressive average accuracy rate of up to 95.47% and an average ARI score of 0.95. Additionally, the number of iterations required is reduced compared to the conventional K-Means algorithm. By introducing innovative techniques, this research provides significant contributions to the field of data clustering, particularly in addressing modern data-driven clustering challenges.\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7086878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing classification efficiency in capsule networks through windowed routing: tackling gradient vanishing, dynamic routing, and computational complexity challenges 通过窗口路由提高胶囊网络的分类效率：应对梯度消失、动态路由和计算复杂性挑战

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-11-18 DOI: 10.1007/s40747-024-01640-8

Gangqi Chen, Zhaoyong Mao, Junge Shen, Dongdong Hou

引用次数: 0