检索结果-内蒙古大学图书馆

2024 International Conference on Cyber-Physical Social intelligence, ICCSI 2024

作者： Chen, Sisi Chen, Xiaofeng Li, Weikai Chongqing Jiaotong University Department of Mathematics Chongqing China Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)9798350376739

Spiking Neural Networks (SNNs), driven by spike-based mechanisms, are known for their high efficiency and low energy consumption, which makes them ideal for applications like image classification, object detection, and medical diagnostics. However, challenges include training difficulties due to the non-differentiability of spiking activities and limitations in encoding schemes, which struggle to capture complex spatio-temporal dynamics. In this paper, we propose a novel algorithm, termed the Spatio-Temporal Backpropagation algorithm with Multi-Scale Attention Coding (STBP-MSC). STBP-MSC dynamically allocates attention across spatial and temporal domains, which improves coding efficiency and enhances the classification accuracy of SNNs. Furthermore, STBP-MSC incorporates the Complementary Leaky Integrate-and-Fire (CLIF) neuron model, which enhances temporal gradient computation. Experimental results on the MNIST and Fashion-MNIST datasets demonstrate that STBP-MSC achieves high coding efficiency and superior performance within minimal time windows. © 2024 IEEE.

关键词： Network coding

来源：评论

学校读者我要写书评

暂无评论

Robust AUC maximization for classification with pairwise confidence comparisons

引用

Frontiers of Computer Science 2024年第4期18卷 73-83页

作者： Haochen SHI Mingkun XIE Shengjun HUANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China

Supervised learning often requires a large number of labeled examples,which has become a critical bottleneck in the case that manual annotating the class labels is *** mitigate this issue,a new framework called pairwise comparison(Pcomp)classification is proposed to allow training examples only weakly annotated with pairwise comparison,i.e.,which one of two examples is more likely to be *** previous study solves Pcomp problems by minimizing the classification error,which may lead to less robust model due to its sensitivity to class *** this paper,we propose a robust learning framework for Pcomp data along with a pairwise surrogate loss called *** provides an unbiased estimator to equivalently maximize AUC without accessing the precise class ***,we prove the consistency with respect to AUC and further provide the estimation error bound for the proposed *** studies on multiple datasets validate the effectiveness of the proposed method.

关键词： method pairwise weakly

来源：评论

学校读者我要写书评

暂无评论

Class-aware Learning for Imbalanced Multi-Label Classification 5

Class-aware Learning for Imbalanced Multi-Label Classificati...

引用

5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Chen, Jiayao Li, Shaoyuan Nanjing University of Aeronautics and Astronautics Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)9798350310603

Imbalanced multi-label image classification has gained increasing attention recently, in which each sample has multiple class labels, but the number of each category is unevenly distributed. It's common in practical applications but traditional multi-label learning methods can hardly deal with imbalance problems. In this paper, we propose an effective method to tackle imbalanced multi-label learning. The class-aware embedding network is proposed to learn robust class-based representation. Additionally, by using the distribution-balanced loss to weigh different samples, our model can improve the feature learning ability of minority classes. Extensive experiments on widely used long-tailed manual multi-label datasets like VOC-LT and COCO-LT explicitly validate the proposed good method. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Double Regularization Loss Based on Long-tailed Noisy Labels 5

A Double Regularization Loss Based on Long-tailed Noisy Labe...

引用

5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Wang, Lei Li, Shaoyuan Nanjing University of Aeronautics and Astronautics Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)9798350310603

Extensive research has been conducted in recent years to solve the long-tailed distribution and achieved excellent results. However, in contrast to well-designed data, datasets with label noise are common in the real world, even in long-tailed datasets. Then, loss functions that rely on prior knowledge of correct labels for long-tailed distributions will fail. To solve the above problems, the robustness of different loss functions in long-tailed data containing noise is first analyzed. Then algorithmic improvements are made to the LDAM loss, which is focused on dealing with long-tailed problems. We propose a robust loss function DRL, which solves the noisy long-tailed problem with two regularisation terms: label regularisation and sample regularisation. Experiments on several datasets validate the effectiveness of the proposed loss function. © 2023 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Faster Double Adaptive Gradient Methods 39

Faster Double Adaptive Gradient Methods

引用

39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Huang, Feihu Luo, Yuning College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (纸本)157735897X

In this paper, we propose a class of faster double adaptive gradient methods to solve nonconvex finite-sum optimization problems possibly with nonsmooth regularization by simultaneously using adaptive learning rate and adaptive mini-batch size. Specifically, we first propose a double adaptive stochastic gradient method (i.e., 2AdaSGD), and prove that our 2AdaSGD obtains a low stochastic first-order oracle (SFO) complexity for finding a stationary solution under the population smoothness condition. Furthermore, we propose a variance reduced double adaptive stochastic gradient method (i.e., 2AdaSPIDER), and prove that our 2AdaSPIDER obtains an optimal SFO complexity under the average smoothness condition, which is lower than the SFO complexity of the existing double adaptive gradient algorithms. In particular, we introduce a new stochastic gradient mapping to adaptively adjust mini-batch size in our stochastic gradient methods. We conduct some numerical experiments to verify efficiency of our proposed methods. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Faster Adaptive Decentralized Learning Algorithms 41

Faster Adaptive Decentralized Learning Algorithms

引用

41st International Conference on machine Learning, ICML 2024

作者： Huang, Feihu Zhao, Jianyu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

Decentralized learning recently has received increasing attention in machine learning due to its advantages in implementation simplicity and system robustness, data privacy. Meanwhile, the adaptive gradient methods show superior performances in many machine learning tasks such as training neural networks. Although some works focus on studying decentralized optimization algorithms with adaptive learning rates, these adaptive decentralized algorithms still suffer from high sample complexity. To fill these gaps, we propose a class of faster adaptive decentralized algorithms (i.e., AdaMDOS and AdaMDOF) for distributed nonconvex stochastic and finite-sum optimization, respectively. Moreover, we provide a solid convergence analysis framework for our methods. In particular, we prove that our AdaMDOS obtains a near-optimal sample complexity of Õ(ϵ-3) for finding an ϵ-stationary solution of nonconvex stochastic optimization. Meanwhile, our AdaMDOF obtains a near-optimal sample complexity of O(√nϵ-2) for finding an ϵ-stationary solution of for nonconvex finite-sum optimization, where n denotes the sample size. To the best of our knowledge, our AdaMDOF algorithm is the first adaptive decentralized algorithm for nonconvex finite-sum optimization. Some experimental results demonstrate efficiency of our algorithms. Copyright 2024 by the author(s)

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A mesh optimization method using machine learning technique and variational mesh adaptation

引用

Chinese Journal of Aeronautics 2022年第3期35卷 27-41页

作者： Tingfan WU Xuejun LIU Wei AN Zenghui HUANG Hongqiang LYU MIITKey Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical *** light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational *** the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD ***,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is *** regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression *** the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area *** examples are first used to verify the feasibility and effectiveness of the proposed *** then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our *** show that the proposed method significantly improves the accuracy of the local flow features on the adaptive *** work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.

关键词： CFD Flow field machine learning Moving mesh method Regression models Variational mesh adaptation

来源：评论

学校读者我要写书评

暂无评论

Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization 41

Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimizat...

引用

41st International Conference on machine Learning, ICML 2024

作者： Huang, Feihu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

Bilevel optimization is widely applied in many machine learning tasks such as hyper-parameter learning, meta learning and reinforcement learning. Although many algorithms recently have been developed to solve the bilevel optimization problems, they generally rely on the (strongly) convex lower-level problems. More recently, some methods have been proposed to solve the nonconvex-PL bilevel optimization problems, where their upper-level problems are possibly nonconvex, and their lower-level problems are also possibly nonconvex while satisfying Polyak-Lojasiewicz (PL) condition. However, these methods still have a high convergence complexity or a high computation complexity such as requiring compute expensive Hessian/Jacobian matrices and its inverses. In the paper, thus, we propose an efficient Hessian/Jacobian-free method (i.e., HJFBiO) with the optimal convergence complexity to solve the nonconvex-PL bilevel problems. Theoretically, under some mild conditions, we prove that our HJFBiO method obtains an optimal convergence rate of O(1/T), where T denotes the number of iterations, and has an optimal gradient complexity of O(ϵ-1) in finding an ϵ-stationary solution. We conduct some numerical experiments on the bilevel PL game and hyper-representation learning task to demonstrate efficiency of our proposed method. Copyright 2024 by the author(s)

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

machine Learning for Brain Imaging Genomics Methods:A Review

引用

machine intelligence Research 2023年第1期20卷 57-78页

作者： Mei-Ling Wang Wei Shao Xiao-Ke Hao Dao-Qiang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information TechnologyNanjing 211106China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China

In the past decade,multimodal neuroimaging and genomic techniques have been increasingly *** an interdiscip-linary topic,brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotyp-ic measures derived from structural and functional brain *** technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in *** is well known that machine learn-ing is a powerful tool in the data-driven association studies,which can fully utilize priori knowledge(intercorrelated structure informa-tion among imaging and genetic data)for association *** addition,the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is *** this paper,the related background and fundamental work in imaging genomics are first ***,we show the univari-ate learning approaches for association analysis,summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning,and present methods for joint association analysis and outcome ***,this paper discusses some prospects for future work.

关键词： Brain imaging genomics machine learning multivariate analysis association analysis outcome prediction

来源：评论

学校读者我要写书评

暂无评论

Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends 37

Beyond Myopia: Learning from Positive and Unlabeled Data thr...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Wang, Xinrui Wan, Wenhai Geng, Chuanxing Li, Shaoyuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

ISBN: (纸本)9781713899921

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns. Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to 11.3% in key metrics. The code is available at https://***/wxr99/HolisticPU. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Iterative methods

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：