检索结果-内蒙古大学图书馆

4th International Workshop on Human Brain and Artificial intelligence, HBAI 2024

作者： Cao, Siyuan Gao, Shuzhan Liang, Chuang Calhoun, Vince D. Wen, Xuyun Fu, Zening Wu, Lei Jiang, Rongtao Zhang, Daoqiang Qi, Shile Xu, Xijia Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Department of Psychiatry Affiliated Nanjing Brain Hospital Nanjing Medical University Nanjing China Department of Psychiatry Nanjing Brain Hospital Medical School Nanjing University Nanjing China Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9789819640003

Schizophrenia (SZ) is a severe mental illness, with 20%-40% exhibit an inadequate or poor response to the first-line antipsychotic drugs (treatment-resistant schizophrenia, TR-SZ). However, the neural mechanisms underlying this treatment-resistance in SZ remain unclear. This study aimed to identify the co-varying multimodal pattern that distinguishing among TR-SZ, non-treatment-resistant schizophrenia (NTR-SZ) and healthy controls (HC) by unsupervised fusion, in which fractional amplitude of low-frequency fluctuation (fALFF), fraction anisotropic (FA), and gray matter volume (GMV) were used as fusion input. 63 TR-SZs, 221 NTR-SZs, and 86 healthy controls (HCs) were included in this study. The joint multimodal components (including fALFF_IC3, FA_IC12, GMV_IC9) were identified that group discriminating among TR-SZ, NTR-SZ and HC (TR-SZ © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： CEN Co-varying multimodal pattern Multimodal Fusion SAN Schizophrenia Treatment-resistance

来源：评论

学校读者我要写书评

暂无评论

Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

引用

Frontiers of Computer Science 2020年第5期14卷 99-112页

作者： Lei Chen Kai Shao Xianzhong Long Lingsheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210023China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and AstronauticsNanjing211106China

Survival analysis aims to predict the occurrence time of a particular event of interest,which is crucial for the prognosis analysis of ***,due to the limited study period and potential losing tracks,the observed data inevitably involve some censored instances,and thus brings a unique challenge that distinguishes from the general regression *** addition,survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size *** address these challenges,we propose a novel multi-task regression learning model,i.e.,prior information guided transductive matrix completion(PigTMC)model,to predict the survival status of the new ***,we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples,and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size *** addition,we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival ***,we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC ***,the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

关键词： survival analysis matrix completion multi-task regression transductive learning multi-task feature selection

来源：评论

学校读者我要写书评

暂无评论

Learning multi-tasks with inconsistent labels by using auxiliary big task

引用

Frontiers of Computer Science 2023年第5期17卷 119-132页

作者： Quan FENG Songcan CHEN College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among *** MTL works mainly focus on the scenario where label sets among multiple tasks(MTs)are usually the same,thus they can be utilized for learning across the ***,the real world has more general scenarios in which each task has only a small number of training samples and their label sets are just partially overlapped or even *** such MTs is more challenging because of less correlation information available among these *** this,we propose a framework to learn these tasks by jointly leveraging both abundant information from a learnt auxiliary big task with sufficiently many classes to cover those of all these tasks and the information shared among those partiallyoverlapped *** our implementation of using the same neural network architecture of the learnt auxiliary task to learn individual tasks,the key idea is to utilize available label information to adaptively prune the hidden layer neurons of the auxiliary network to construct corresponding network for each task,while accompanying a joint learning across individual *** experimental results demonstrate that our proposed method is significantly competitive compared to state-of-the-art methods.

关键词： multi-task learning inconsistent labels auxiliary task

来源：评论

学校读者我要写书评

暂无评论

Sequential Cooperative Distillation for Imbalanced Multi-Task Learning

引用

Journal of Computer Science & Technology 2024年第5期39卷 1094-1106页

作者： Quan Feng Jia-Yu Yao Ming-Kun Xie Sheng-Jun Huang Song-Can Chen College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing 211106China

Multi-task learning(MTL)can boost the performance of individual tasks by mutual learning among multiple related ***,when these tasks assume diverse complexities,their corresponding losses involved in the MTL objective inevitably compete with each other and ultimately make the learning biased towards simple tasks rather than complex *** address this imbalanced learning problem,we propose a novel MTL method that can equip multiple existing deep MTL model architectures with a sequential cooperative distillation(SCD)***,we first introduce an efficient mechanism to measure the similarity between tasks,and group similar tasks into the same block to allow their cooperative learning from each *** on this,the grouped task blocks are sorted in a queue to determine the learning sequence of the tasks according to their complexities estimated with the defined performance ***,a distillation between the individual task-specific models and the MTL model is performed block by block from complex to simple manner,achieving a balance between competition and cooperation among learning multiple *** experiments demonstrate that our method is significantly more competitive compared with state-of-the-art methods,ranking No.1 with average performances across multiple datasets by improving 12.95%and 3.72%compared with OMTL and MTLKD,respectively.

关键词： multi-task learning(MIT) imbalanced learning similarity estimation knowledge distillation distillation queue

来源：评论

学校读者我要写书评

暂无评论

A comprehensive perspective of contrastive self-supervised learning

引用

Frontiers of Computer Science 2021年第4期15卷 1-3页

作者： Songcan CHEN Chuanxing GENG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China

Self-supervised learning(SSL),as a new unsupervised representation learning paradigm in machine learning,recently has received extensive attention,which is also regarded as the future of machine learning by the Turing Award winner,LeCunn[1].SSL learns representation from unlabeled data using"pretext"tasks that provide free supervision,with the aim of performing well on semantic relation agnostic downstream(supervised)*** usually divides two stages:first learning as general/invariant representation/feature as possible with the auto-annotation pretext tasks(the core),then transferring the learned knowledge to downstream tasks(the ultimate goal)[2].

关键词： SSL representation Turing

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

On the learning dynamics of two-layer quadratic neural networks for understanding deep learning

引用

Frontiers of Computer Science 2022年第3期16卷 77-82页

作者： Zhenghao TAN Songcan CHEN College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing 211106China

Deep learning performs as a powerful paradigm in many real-world applications;however,its mechanism remains much of a *** gain insights about nonlinear hierarchical deep networks,we theoretically describe the coupled nonlinear learning dynamic of the two-layer neural network with quadratic activations,extending existing results from the linear *** quadratic activation,although rarely used in practice,shares convexity with the widely used ReLU activation,thus producing similar *** this work,we focus on the case of a canonical regression problem under the standard normal distribution and use a coupled dynamical system to mimic the gradient descent method in the sense of a continuous-time limit,then use the high order moment tensor of the normal distribution to simplify these ordinary differential *** simplified system yields unexpected fixed *** existence of these non-global-optimal stable points leads to the existence of saddle points in the loss surface of the quadratic *** analysis shows there are conserved quantities during the training of the quadratic *** quantities might result in a failed learning process if the network is initialized ***,We illustrate the comparison between the numerical learning curves and the theoretical one,which reveals the two alternately appearing stages of the learning process.

关键词： learning dynamic quadratic network ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

Bayesian compressive principal component analysis

引用

Frontiers of Computer Science 2020年第4期14卷 29-38页

作者： Di MA Songcan CHEN College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing211106China

Principal component analysis(PCA)is a widely used method for multivariate data analysis that projects the original high-dimensional data onto a low-dimensional subspace with maximum ***,in practice,we would be more likely to obtain a few compressed sensing(CS)measurements than the complete high-dimensional data due to the high cost of data acquisition and *** this paper,we propose a novel Bayesian algorithm for learning the solutions of PCA for the original data just from these CS *** this end,we utilize a generative latent variable model incorporated with a structure prior to model both sparsity of the original data and effective dimensionality of the latent *** proposed algorithm enjoys two important advantages:1)The effective dimensionality of the latent space can be determined automatically with no need to be pre-specified;2)The sparsity modeling makes us unnecessary to employ multiple measurement matrices to maintain the original data space but a single one,thus being storage *** results on synthetic and real-world datasets show that the proposed algorithm can accurately learn the solutions of PCA for the original data,which can in turn be applied in reconstruction task with favorable results.

关键词： compressed sensing principal component analysis Bayesian learning sparsity modeling

来源：评论

学校读者我要写书评

暂无评论

An Improved Algorithm for Spiking Neural Networks with Multi-Scale Attention Coding

An Improved Algorithm for Spiking Neural Networks with Multi...

引用

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

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：