检索结果-内蒙古大学图书馆

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

来源：评论

学校读者我要写书评

暂无评论

Small Object Detection using Multi-scale Feature Fusion and Attention 41

Small Object Detection using Multi-scale Feature Fusion and ...

引用

第41届中国控制会议

作者： Baokai Liu Shiqiang Du Jiacheng Li Jianhua Wang Wenjie Liu Key Laboratory of Chinas Ethnic Languages and Information Technology of Ministry of Education Chinese National Information Technology Research InstituteNorthwest Minzu University College of Mathematics and Computer Science Northwest Minzu University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In deep learning,object detection has achieved a very large performance ***,due to the few features available for small objects,network structure,sample imbalance and other reasons,the result is unsatisfactory in small object *** order to solve this problem,this paper proposes a method based on the combination of mutil-scale feature fusion and dilated convolution,which uses dilated convolution to expand the receptive field of feature maps at different scales and then extracts the high-level semantic information and low-level semantic information from the backbone *** obtained feature maps of different receptive fields are fused to obtain the final feature map prediction *** addition,we add a series of channel attention and spatial attention mechanisms to the network to better obtain the context information of the object in the *** show that this method can have higher accuracy than the traditional YOLOv3 network in the detection of small *** addition,the size of 640*640 images,we can achieve 31.5% accuracy in the detection of small objects in MS *** with YOLOv5,there are 4 points of improvement.

关键词： Attention Mechanism Dilated Convolution Multi-scale Feature Fusion Channel Attention Spatial Attention

来源：评论

学校读者我要写书评

暂无评论

Multi-view Spectral Clustering Based on Graph Learning 41

Multi-view Spectral Clustering Based on Graph Learning

引用

第41届中国控制会议

作者： Jinmei Song Baokai Liu Kaiwu Zhang Yao Yu Shiqiang Du Key Laboratory of Chinas Ethnic Languages and Information Technology of Ministry of Education Chinese National Information Technology Research InstituteNorthwest Minzu University College of Mathematics and Computer Science Northwest Minzu University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Multi-view clustering is based on the exploration of complementary information from different *** graphbased approach is a representative method among existing multi-view clustering ***,how to effectively obtain complementary information in multi-view data and how to perform effective clustering in the face of the low quality of the initially constructed data graph is still a *** address the aforementioned issues,we propose a new multi-view spectral clustering model,namely multi-view spectral clustering based on graph learning(GLSC),which utilizes the relevant knowledge of self-representing attributes to construct similarity graphs that can represent the relationship between data,uses the constrained Laplacian rank method to construct an affinity graph for the similarity graphs,and applies the classical spectral clustering method for *** propose an iterative algorithm to solve the optimization ***,we conduct extensive experiments on four real benchmark datasets,and the experimental results show the superiority of GLSC clustering performance.

关键词： Multi-view Clustering Spectral Clustering Graph Learning Constrained Laplacian Rank

来源：评论

学校读者我要写书评

暂无评论

ProofInfer: Generating Proof via Iterative Hierarchical Inference

ProofInfer: Generating Proof via Iterative Hierarchical Infe...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Fei, Zichu Zhang, Qi Zhou, Xin Gui, Tao Huang, Xuanjing School of Computer Science Fudan Unviersity China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

Proof generation focuses on deductive reasoning: given a hypothesis and a set of theories, including some supporting facts and logical rules expressed in natural language, the model generates a proof tree indicating how to deduce the hypothesis from given theories. Current models with state-of-the-art performance employ the stepwise method, linking an individual node to the proof step-by-step. However, these methods actually focus on generating several proof paths rather than a whole tree. To address this problem, we propose ProofInfer, which generates the proof tree via iterative hierarchical inference. At each step, ProofInfer generates the entire layer for proof tree, where all nodes in this layer are generated simultaneously. Since the conventional autoregressive generation architecture cannot simultaneously predict multiple nodes, ProofInfer employs text-to-text paradigm to avoid it. To this end, we propose a divide- and-conquer algorithm to encode the proof tree as the plain text recursively without structure information loss. Experimental results show that ProofInfer significantly outperforms the state-of-the-art (SOTA) models on several widely-used datasets. In addition, ProofInfer still performs well with data-limited, achieving comparable performance to the SOTA models with only 40% of the training data. © 2022 Association for Computational Linguistics.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases 29

LFKQG: A Controlled Generation Framework with Local Fine-tun...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Fei, Zichu Zhou, Xin Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan Unviersity China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

Question generation over knowledge bases (KBQG) aims to generate natural questions about a subgraph that can be answered by a given answer entity. Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph. (2) There are a large number of out-of-vocabulary (OOV) predicates in real-world scenarios, which are hard to adapt for most KBQG models. To address these challenges, we propose LFKQG, a controlled generation framework for Question Generation over Knowledge Bases. (1) LFKQG employs a simple controlled generation method to generate the questions containing the critical entities in the subgraph, ensuring the question is relevant to the whole subgraph. (2) We propose an optimization strategy called local fine-tuning, which makes good use of the rich information hidden in the pre-trained model to improve the ability of the model to adapt the OOV predicates. Extensive experiments show that our method outperforms existing methods greatly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestions . © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HCMonitor: An accurate measurement system for high concurrent network services

HCMonitor: An accurate measurement system for high concurren...

引用

作者： Song, Hui Zhang, Wenli Liu, Ke Shen, Yifan Chen, Mingyu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Frontier Research Center Peng Cheng Laboratory Shenzhen China

This article aims to enhance the monitoring accuracy of high concurrent network services. As modern network services grow rapidly in data centers, tail latency has become one of the most crucial deciding factors on user experience. Latency measurement and anomaly detection are essential in evaluating service performance. Existing monitoring tools can be divided into two categories according to estimation methods. First, approaches based on sample traffic sample network packets to unburden the measurement. Second, approaches based on full traffic like wrk, analyze all of the packets from the kernel network stack and load the client-side overhead into response delay. Therefore, we propose a high-performance monitor system named HCMonitor, which computes the server-side response latency and the round-trip time of per-request. It can afford full traffic monitoring on the basis of userspace, "zero copy" and pipeline. By switch mirroring, the measured latency eliminates the kernel network stack overhead and the queuing delay of the client-side. Such measurement results in improved accuracy, online analysis, anomaly detection, real-time display and transparent to network services. Our evaluations show HCMonitor obtains a higher throughput compared with tcpdump by over 200 times. Compared with wrk, the tail latency accuracy shows an increase by up to 72%–76% in high concurrent networks. © 2021 John Wiley & Sons Ltd.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER 29

Read Extensively, Focus Smartly: A Cross-document Semantic E...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Zhao, Jun Zhao, Xin Zhan, Wenyu Gui, Tao Zhang, Qi Qiao, Liang Cheng, Zhanzhan Pu, Shiliang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University China Hikvision Research Institute Shanghai China

The introduction of multimodal information and pretraining technique significantly improves entity recognition from visually-rich documents. However, most of the existing methods pay unnecessary attention to irrelevant regions of the current document while ignoring the potentially valuable information in related documents. To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document. 2) To further enrich the entity-related context, we propose a cross-document information awareness technique, which enables the model to collect more evidence across documents to assist in prediction. The experimental results on two documents understanding benchmarks covering eight languages demonstrate that our method outperforms the SOTA methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning 60

Flooding-X: Improving BERT's Resistance to Adversarial Attac...

引用

60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Liu, Qin Zheng, Rui Bao, Rong Liu, Jingyi Liu, ZhiHua Cheng, Zhanzhan Qiao, Liang Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Hikvision Research Institute Hangzhou China

ISBN: (纸本)9781955917216

Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training. However, the tradition of generating adversarial perturbations for each input embedding (in the settings of NLP) scales up the training computational complexity by the number of gradient steps it takes to obtain the adversarial samples. To address this problem, we leverage Flooding method which primarily aims at better generalization and we find promising in defending adversarial attacks. We further propose an effective criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. Our approach requires zero adversarial sample for training, and its time consumption is equivalent to fine-tuning, which can be 2-15 times faster than standard adversarial training. We experimentally show that our method improves BERT's resistance to textual adversarial attacks by a large margin, and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. © 2022 Association for Computational Linguistics.

关键词： Floods

来源：评论

学校读者我要写书评

暂无评论

EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

arXiv

引用

arXiv 2024年

作者： Zhou, Weikang Wang, Xiao Xiong, Limao Xia, Han Gu, Yingshuang Chai, Mingxu Zhu, Fukang Huang, Caishuang Dou, Shihan Xi, Zhiheng Zheng, Rui Gao, Songyang Zou, Yicheng Yan, Hang Le, Yifan Wang, Ruohui Li, Lijun Shao, Jing Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai AI Laboratory China

Warning: This paper contains examples of harmful language, and reader discretion is recommended. Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibited outputs. However, due to significant differences among various jailbreak methods, there is no standard implementation framework available for the community, which limits comprehensive security evaluations. This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs. It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator. This modular framework enables researchers to easily construct attacks from combinations of novel and existing components. So far, EasyJailbreak supports 11 distinct jailbreak methods and facilitates the security validation of a broad spectrum of LLMs. Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks. Notably, even advanced models like GPT-3.5-Turbo and GPT-4 exhibit average Attack Success Rates (ASR) of 57% and 33%, respectively. We have released a wealth of resources for researchers, including a web platform 1, PyPI published package 2, screencast video3, and experimental outputs 4 Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Aware: Adaptive Distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

The accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. This paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. This paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. The results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 IEEE.

关键词： Deep learning

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：