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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Improving the robustness of ISOMAP by de-noising

Improving the robustness of ISOMAP by de-noising

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International Joint Conference on Neural Networks (IJCNN)

作者： Bo Li De-Shuang Huang Chao Wang University of Science and Technology of China Hefei Anhui CN Intell. Comput. Lab. Chinese Acad. of Sci. Hefei Department of Automation University of Science and Technology Hefei Anhui China Intelligent Computing Lab Institute of Intelligent Machine Chinese Academy and Sciences Hefei Anhui China

ISOMAP is a manifold learning based algorithm for dimensionality reduction, which is successfully applied to data visualization. However, there exists such limitation in classical ISOMAP that the algorithm is sensitive to noises, especially outliers. So in this paper an extended ISOMAP algorithm is put forward to solve the problem of sensitivity. The proposed algorithm follows the method of classical ISOMAP except that a preprocessing strategy is introduced to remove the noises and outliers. The likelihood of each point to be a noise or an outlier is quantified by carrying out weighted principal component analysis and box statistics method is adopted to distinguish clear points from noisy ones, then ISOMAP can be performed after de-noising. Experiments on noisy s-curve and noisy Swiss-roll data validate its efficiency for improving robustness.

关键词： Noise Robustness Noise measurement Distance measurement Signal to noise ratio Principal component analysis Manifolds

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Constrained Maximum Variance Mapping

Constrained Maximum Variance Mapping

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International Joint Conference on Neural Networks (IJCNN)

作者： Bo Li De-Shuang Huang Kun-Hong Liu University of Science and Technology of China Hefei Anhui CN Intell. Comput. Lab. Chinese Acad. of Sci. Hefei Department of Automation University of Science and Technology Hefei Anhui China Intelligent Computing Lab Institute of Intelligent Machine Chinese Academy and Sciences Hefei Anhui China

In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold lab.ls into account. After the local scatters have been characterized, the proposed method focuses on developing a linear transformation that can maximize the distances matrix between all the manifolds under the constraint of locality preserving. Then, YALE face database, ORL face database are all taken to examine the effectiveness and efficiency of the proposed method. Experimental results validate that the proposed approach is superior to other widely used feature extraction methods.

关键词： Manifolds Principal component analysis Face Feature extraction Classification algorithms Databases Geometry

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nnDetection: A Self-configuring Method for Medical Object Detection

arXiv

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arXiv 2021年

作者： Baumgartner, Michael Jäger, Paul F. Isensee, Fabian Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Interactive Machine Learning Group German Cancer Research Center Germany HIP Applied Computer Vision Lab. German Cancer Research Center Germany Pattern Analysis and Learning Group Heidelberg University Hospital Germany

Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection. The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art. We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://***/MIC-DKFZ/nnDetection. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Prior Visual Relationship Reasoning For Visual Question Answering

Prior Visual Relationship Reasoning For Visual Question Answ...

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IEEE International Conference on Image Processing

作者： Zhuoqian Yang Zengchang Qin Jing Yu Tao Wan Robotics Institute Carnegie Mellon University Pittsburgh PA USA Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China Institute of Information Engineering CAS China School of Biological Science and Medical Engineering Beihang University Beijing China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Visual Question Answering (VQA) is a representative task of cross-modal reasoning where an image and a free-form question in natural language are presented and the correct answer needs to be determined using both visual and textual information. One of the key issues of VQA is to reason with semantic clues in the visual content under the guidance of the question. In this paper, we propose Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and their semantic relations for the correct answer. The visual relationship is projected into a deep learned semantic space constrained by visual context and language priors. Based on comprehensive experiments on two challenging datasets: GQA and VQA 2.0, we demonstrate the effectiveness and interpretability of the new model.

关键词： Visualization Semantics Convolution Cognition Knowledge discovery Benchmark testing Measurement

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Double variance reduction: a smoothing trick for composite optimization problems without first-order gradient 24

Double variance reduction: a smoothing trick for composite o...

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Proceedings of the 41st International Conference on machine learning

作者： Hao Di Haishan Ye Yueling Zhang Xiangyu Chang Guang Dai Ivor W. Tsang Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China and SGIT AI Lab State Grid Corporation of China International Business School Beijing Foreign Studies University Beijing China Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China CFAR and IHPC Agency for Science Technology and Research (A*STAR) Singapore and College of Computing and Data Science NTU Singapore

Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods. However, in composite optimization problems, ZO methods encounter an additional variance called the coordinate-wise variance, which stems from the random gradient estimation. To reduce this variance, prior works require estimating all partial derivatives, essentially approximating FO information. This approach demands O(d) function evaluations (d is the dimension size), which incurs substantial computational costs and is prohibitive in high-dimensional scenarios. This paper proposes the Zeroth-order Proximal Double Variance Reduction (ZPDVR) method, which utilizes the averaging trick to reduce both sampling and coordinate-wise variances. Compared to prior methods, ZPDVR relies solely on random gradient estimates, calls the stochastic zeroth-order oracle (SZO) in expectation O(1) times per iteration, and achieves the optimal O(d(n+κ) log(1/ε)) SZO query complexity in the strongly convex and smooth setting, where κ represents the condition number and ε is the desired accuracy. Empirical results validate ZPDVR's linear convergence and demonstrate its superior performance over other related methods.

关键词：

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A novel texture image segmentation model based on multi-scale structure

A novel texture image segmentation model based on multi-scal...

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International Conference on Multisensor Fusion and Integration for intelligent Systems (MFI)

作者： Hai Min Xiao-Feng Wang De-Shuang Huang Department of Automation University of Science and Technology of China Hefei Anhui China Intelligent Computing Lab Chinese Academy of Sciences Department of Computer Science and Technology Hefei University Hefei Anhui China Machine Learning and Systems Biology Laboratory Tongji University Shanghai China

ISBN: (纸本)9781479967339

Using neighborhood information from different scales in an effective way for object segmentation, however, remains a difficult problem. In this paper, we propose a novel method which incorporates the multi-scale structure into intensity information. The multi-scale structure is proposed as structure information to describe the texture feature of image. We select eight points as the sample components of each layer and further solve the maximum variance between eight components and center pixel. In addition, the multi-scale structure can be suitably used to extract the texture information of image. Then, by combining texture information with intensity information, we use level set method to obtain the segmentation results. Experiments using synthetic and real natural scene images demonstrate the superior performance of the proposed method.

关键词： Image segmentation Level set Feature extraction Gabor filters Tensile stress Visualization Image color analysis

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DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue

arXiv

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arXiv 2020年

作者： Jiang, Xiaoze Yu, Jing Sun, Yajing Qin, Zengchang Zhu, Zihao Hu, Yue Wu, Qi Institute of Information Engineering Chinese Academy of Sciences Beijing China Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China AI Research Codemao Inc University of Adelaide Australia

Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation. In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility. In this architecture, word generation is decomposed into a series of attention-based information selection steps, performed by the novel recurrent Deliberation, Abandon and Memory (DAM) module. Each DAM module performs an adaptive combination of the response-level semantics captured from the encoder and the word-level semantics specifically selected for generating each word. Therefore, the responses contain more detailed and non-repetitive descriptions while maintaining the semantic accuracy. Furthermore, DAM is flexible to cooperate with existing visual dialogue encoders and adaptive to the encoder structures by constraining the information selection mode in DAM. We apply DAM to three typical encoders and verify the performance on the VisDial v1.0 dataset. Experimental results show that the proposed models achieve new state-of-the-art performance with high-quality responses. The code is availab.e at https://***/JXZe/DAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient

arXiv

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arXiv 2024年

作者： Di, Hao Ye, Haishan Zhang, Yueling Chang, Xiangyu Dai, Guang Tsang, Ivor W. Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China China International Business School Beijing Foreign Studies University Beijing China Singapore College of Computing and Data Science NTU Singapore

Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods. However, in composite optimization problems, ZO methods encounter an additional variance called the coordinate-wise variance, which stems from the random gradient estimation. To reduce this variance, prior works require estimating all partial derivatives, essentially approximating FO information. This approach demands O(d) function evaluations (d is the dimension size), which incurs substantial computational costs and is prohibitive in high-dimensional scenarios. This paper proposes the Zeroth-order Proximal Double Variance Reduction (ZPDVR) method, which utilizes the averaging trick to reduce both sampling and coordinate-wise variances. Compared to prior methods, ZPDVR relies solely on random gradient estimates, calls the stochastic zeroth-order oracle (SZO) in expectation O(1) times per iteration, and achieves the optimal O(d(n+κ) log( 1∈ )) SZO query complexity in the strongly convex and smooth setting, where κ represents the condition number and ∈ is the desired accuracy. Empirical results validate ZPDVR's linear convergence and demonstrate its superior performance over other related methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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Can Gaussian sketching converge faster on a preconditioned landscape? 24

Can Gaussian sketching converge faster on a preconditioned l...

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Proceedings of the 41st International Conference on machine learning

作者： Yilong Wang Haishan Ye Guang Dai Ivor W. Tsang Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China and SGIT AI Lab State Grid Corporation of China SGIT AI Lab State Grid Corporation of China CFAR and IHPC Agency for Science Technology and Research (A*STAR) Singapore and College of Computing and Data Science NTU Singapore

This paper focuses on the large-scale optimization which is very popular in the big data era. The gradient sketching is an important technique in the large-scale optimization. Specifically, the random coordinate descent algorithm is a kind of gradient sketching method with the random sampling matrix as the sketching matrix. In this paper, we propose a novel gradient sketching called GSGD (Gaussian Sketched Gradient Descent). Compared with the classical gradient sketching methods such as the random coordinate descent and SEGA (Hanzely et al., 2018), our GSGD does not require the importance sampling but can achieve a fast convergence rate matching the ones of these methods with importance sampling. Furthermore, if the objective function has a non-smooth regularization term, our GSGD can also exploit the implicit structure information of the regularization term to achieve a fast convergence rate. Finally, our experimental results substantiate the effectiveness and efficiency of our algorithm.

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