检索结果-内蒙古大学图书馆

Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bhushan Fulkar Rushikesh Burle Pawan Patil Sanskruti Gaurkhade Swapnil Gundewar Utkarsha Pacharaney Department of Artificial Intelligence and Data science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Since diabetic retinopathy (DR) is the primary cause of blindness in working-age adults worldwide, vision loss must be avoided by receiving early detection and treatment. In recent years, deep learning techniques have become more and more useful tools for automated drug discovery, possibly offering solutions to overcome the limitations of traditional screening methods. In-depth descriptions of the most recent deep learning methods for DR detection are provided in this review article. We begin by discussing the importance of DR screening, its prevalence, and the challenges faced by healthcare systems in managing this condition. Next, we discuss different deep learning architectures used for DR detection, such as CNNs and RNNs, and their combinations. We investigate how to improve the precision, resilience, and interpretability of DR detection models through the use of explainable AI, ensemble methods, transfer learning, and attention mechanisms. We also examine publicly available datasets, evaluation metrics, and preprocessing techniques that are commonly used to benchmark DR detection algorithms. We discuss the limitations and possible uses of deep learning-based deep readout detection, emphasizing the significance of large-scale, diverse datasets, the interpretability of the model, and real-world deployment concerns. The overall objectives of this review are to advance the field of automated DR detection by highlighting recent developments, compiling existing knowledge, and identifying areas that require further research.

关键词： Deep learning Diabetic retinopathy Reviews Transfer learning Imaging Transforms Medical services

来源：评论

学校读者我要写书评

暂无评论

Mitigating multiple descents: A model-agnostic framework for risk monotonization

arXiv

引用

arXiv 2022年

作者： Patil, Pratik Kuchibhotla, Arun Kumar Wei, Yuting Rinaldo, Alessandro Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science The Wharton School University of Pennsylvania PhiladelphiaPA19104 United States

Recent empirical and theoretical analyses of several commonly used prediction procedures reveal a peculiar risk behavior in high dimensions, referred to as double/multiple descent, in which the asymptotic risk is a non-monotonic function of the limiting aspect ratio of the number of features or parameters to the sample size. To mitigate this undesirable behavior, we develop a general framework for risk monotonization based on cross-validation that takes as input a generic prediction procedure and returns a modified procedure whose out-of-sample prediction risk is, asymptotically, monotonic in the limiting aspect ratio. As part of our framework, we propose two data-driven methodologies, namely zero- and one-step, that are akin to bagging and boosting, respectively, and show that, under very mild assumptions, they provably achieve monotonic asymptotic risk behavior. Our results are applicable to a broad variety of prediction procedures and loss functions, and do not require a well-specified (parametric) model. We exemplify our framework with concrete analyses of the minimum l2, l1-norm least squares prediction procedures. As one of the ingredients in our analysis, we also derive novel additive and multiplicative forms of oracle risk inequalities for split cross-validation that are of independent interest. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Optimal kernel choice for score function-based causal discovery 24

Optimal kernel choice for score function-based causal discov...

引用

Proceedings of the 41st International Conference on machine learning

作者： Wenjie Wang Biwei Huang Feng Liu Xinge You Tongliang Liu Kun Zhang Mingming Gong School of Mathematics and Statistics The University of Melbourne Australia and Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Halicioğlu Data Science Institute (HDSI) University of California San Diego School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates and Department of Philosophy Carnegie Mellon University

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Theoretical Analysis of Self-Supervised learning for Vision Transformers

arXiv

引用

arXiv 2024年

作者： Huang, Yu Wen, Zixin Chi, Yuejie Liang, Yingbin UPenn United States CMU United States OSU United States Department of Statistics and Data Science Wharton School University of Pennsylvania United States Machine Learning Department Carnegie Mellon University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of Electrical and Computer Engineering The Ohio State University United States

Self-supervised learning (SSL) has become a foundational approach in computer vision, which is broadly categorized into reconstruction-based methods like masked autoencoders (MAE) and discriminative methods such as contrastive learning (CL). Recent empirical observations reveal that MAE and CL capture distinct representations. CL tends to focus on global patterns, while MAE adeptly captures both global and subtle local information simultaneously. Despite a flurry of recent empirical investigations to shed light on this difference, theoretical understanding remains limited, especially on the dominant architecture: vision transformers (ViTs). In this paper, to provide rigorous insights, we model the distribution of visual data by considering two types of spatial features: dominant global features and comparatively minuscule local features and study the impact of imbalance among these features. We analyze the training dynamics of one-layer softmax-based ViTs on both MAE and CL objectives using gradient descent. Our analysis shows that as the degree of feature imbalance varies, ViTs trained with the MAE objective effectively learn both global and local features to achieve near-optimal reconstruction, while the CL-trained ViTs favor predominantly global features, even under mild imbalance. These results provide a theoretical explanation for the different behaviors of MAE and CL observed in prior studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

HUMAN-IN-THE-LOOP: TOWARDS LABEL EMBEDDINGS FOR MEASURING CLASSIFICATION DIFFICULTY

arXiv

引用

arXiv 2023年

作者： Hechinger, Katharina Zhu, Xiao Xiang Koller, Christoph Kauermann, Göran Department of Statistics Ludwig-Maximilians-University Munich Germany Chair of Data Science in Earth Observation Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany German Aerospace Center Wessling Germany

Uncertainty in machine learning models is a timely and vast field of research. In supervised learning, uncertainty can already occur in the first stage of the training process, the annotation phase. This scenario is particularly evident when some instances cannot be definitively classified. In other words, there is inevitable ambiguity in the annotation step and hence, not necessarily a single "ground truth" associated with each instance. The main idea of this work is to drop the assumption of a ground truth label and instead embed the annotations into a multidimensional space. This embedding is derived from the empirical distribution of annotations in a Bayesian setup, modeled via a Dirichlet-Multinomial framework. We estimate the model parameters and posteriors using a stochastic Expectation Maximisation algorithm with Markov Chain Monte Carlo steps. The methods developed in this paper readily extend to various situations where multiple annotators independently label instances. To showcase the generality of the proposed approach, we apply our approach to three benchmark datasets for image classification and Natural Language Inference, where multiple annotations per instance are available. Besides the embeddings, we can investigate the resulting correlation matrices, which reflect the semantic similarities of the original classes very well for all three exemplary datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

New Approach of Artificial Intelligence in Digital Forensic Investigation: A Literature Review 1

引用

International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. Computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing 4

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

引用

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Joon, Rakesh Kumar Anitha, R. Selvan, Saravana Latha Mageshwari, P.S. Gayathri Priya, S. Malathi, K. Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India S.A. Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600077 India School of Professional Engineering Manukau Institute of Technology Auckland2104 New Zealand Tamil Nadu Kavaraipettai601206 India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai601206 India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications. © 2025 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques

arXiv

引用

arXiv 2024年

作者： Khan, Arham Nief, Todd Hudson, Nathaniel Sakarvadia, Mansi Grzenda, Daniel Ajith, Aswathy Pettyjohn, Jordan Chard, Kyle Foster, Ian Department of Computer Science University of Chicago ChicagoIL United States Data Science & Learning Division Argonne National Laboratory LemontIL United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States

Understanding neural networks is crucial to creating reliable and trustworthy deep learning models. Most contemporary research in interpretability analyzes just one model at a time via causal intervention or activation analysis. Yet despite successes, these methods leave significant gaps in our understanding of the training behaviors of neural networks, how their inner representations emerge, and how we can predictably associate model components with task-specific behaviors. Seeking new insights from work in related fields, here we survey literature in the field of model merging, a field that aims to combine the abilities of various neural networks by merging their parameters and identifying task-specific model components in the process. We analyze the model merging literature through the lens of loss landscape geometry, an approach that enables us to connect observations from empirical studies on interpretability, security, model merging, and loss landscape analysis to phenomena that govern neural network training and the emergence of their inner representations. To systematize knowledge in this area, we present a novel taxonomy of model merging techniques organized by their core algorithmic principles. Additionally, we distill repeated empirical observations from the literature in these fields into characterizations of four major aspects of loss landscape geometry: mode convexity, determinism, directedness, and connectivity. We argue that by improving our understanding of the principles underlying model merging and loss landscape geometry, this work contributes to the goal of ensuring secure and trustworthy machine learning in practice. © 2024, CC BY.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Boundedness for the Chemotaxis System with Logistic Growth

SSRN

引用

SSRN 2024年

作者： Zhang, Qian Wu, Yonghong Wang, Peiguang Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Department of Mathematics and Statistics Curtin University PerthWA6845 Australia

In this paper, we consider a mathematical model motivated by the studies of coral broadcastspawning\begin{align}\left\{\begin{aligned}\no\partial_{t} n+u\cdot\nabla n-\Delta n & =-\chi \nabla \cdot(n \nabla c)+n-\epsilon n^{q} \\\no\partial_{t} c+u\cdot\nabla c-\Delta c & =-c+n\end{aligned}\right. \quad \text { in } \mathbb{R}^{d} \times \mathbb{R}^{+},\end{align}where $d\geq1$, $\epsilon>0$, and $q\geq2$. We establish global-in-time well-posedness and boundedness of the solution to the Cauchy problem of this system by developing local-in-space estimates. The crux point of our proof depends intensely on localization in the space of solutions induced by ``local effect" of the $L^\infty(\R^d)$-norm. © 2024, The Authors. All rights reserved.

关键词： Biochemistry

来源：评论

学校读者我要写书评

暂无评论

machine-learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces

arXiv

引用

arXiv 2025年

作者： Delgado, Frederico P. Simões, Frederico Kronik, Leeor Kaiser, Waldemar Egger, David A. Physics Department TUM School of Natural Sciences Technical University of Munich Germany Department of Molecular Chemistry and Materials Science Weizmann Institute of Science Israel Atomistic Modeling Center Munich Data Science Institute Technical University of Munich Germany Munich Center for Machine Learning Munich Germany

The spectacular performance of halide perovskites in optoelectronic devices is rooted in their favorable tolerance to structural defects. Previous studies showed that defects in these materials generate shallow electronic states that do not degrade device performance. However, how these shallow states persist amid the pronounced thermally-stimulated atomic dynamics on halide perovskite surfaces remains unknown. This work reveals that electronic states at surfaces of the prototypical CsPbBr3 variant are energetically distributed at room temperature, akin to well-passivated inorganic semiconductors, even when covalent bonds remain cleaved and undercoordinated. Specifically, a striking tendency for shallow surface states is found with approximately 70% of surface-state energies appearing within 0.2 eV or ≈8kBT from the valence-band edge. Furthermore, we show that even when surface states appear deeper in the gap, they are not energetically isolated and are less likely to act as traps. We achieve this result by accelerating first-principles calculations via machine-learning techniques and show that the unique atomic dynamics in these materials render the formation of deep electronic states at their surfaces unlikely. These findings reveal the microscopic mechanism behind the low density of deep defect states at dynamic halide perovskite surfaces, which is key to their exceptional performance in devices. © 2025, CC BY.

关键词： Perovskite

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：