Article
Mathematics, Interdisciplinary Applications
Xiaobin Yu, Yajun Yin, Rekha Srivastava
Summary: In this study, we delve into the general theory of operator kernel functions in operational calculus and establish a mapping relation between the kernel function and the corresponding operator. This research demonstrates the uniqueness of the kernel function and provides a novel perspective on how operational calculus can be understood and applied. The accuracy of the proposed method is substantiated through consistency tests and its application is illustrated in different structures. These results highlight the importance of this study for the understanding and application of operational calculus.
FRACTAL AND FRACTIONAL
(2023)
Article
Computer Science, Artificial Intelligence
Michael S. Duarte, Guilherme A. Barreto
Summary: The paper introduces a new sparse variant of the Correntropy Kernel Learning model named FADOS-CKL for online system identification in the presence of outliers. With a fully adaptive dictionary of support vectors (SVs) and optimization techniques, FADOS-CKL achieves a balance between reducing dictionary size and maintaining high generalization capability. Performance comparisons with powerful alternatives on large benchmark datasets show the effectiveness of FADOS-CKL in minimizing predictive errors and achieving high sparsity levels.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Shahla Faisal, Gerhard Tutz
Summary: The article discusses the use of multiple imputation methods to address missing values in medical research, particularly in high-dimensional data settings. The proposed method based on nearest neighbors successfully imputes missing values and performs well in simulated data comparisons.
INFORMATION SCIENCES
(2021)
Article
Mathematics, Applied
Yuka Hashimoto, Takashi Nodera
Summary: This paper proposes a novel technique for accelerating the Krylov subspace methods for transfer operators by replacing positive definite kernels in RKHS, which is equivalent to preconditioning the transfer operator with a specific linear operator.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Statistics & Probability
Bharath K. Sriperumbudur, Nicholas Sterge
Summary: Kernel methods are powerful for nonlinear data analysis but suffer from scalability issues in big data scenarios. This paper investigates the efficacy of random feature approximation in kernel principal component analysis (KPCA) and analyzes the trade-off between computational and statistical behaviors.
ANNALS OF STATISTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Pau Batlle, Matthieu Darcy, Bamdad Hosseini, Houman Owhadi
Summary: We present a general kernel-based framework for learning operators between Banach spaces. Our approach is competitive in terms of cost-accuracy trade-off and matches or beats the performance of popular neural net methods on most benchmarks. The framework inherits advantages from kernel methods, such as simplicity, interpretability, convergence guarantees, a priori error estimates, and Bayesian uncertainty quantification.
JOURNAL OF COMPUTATIONAL PHYSICS
(2024)
Article
Mathematics, Applied
Fangming Cai, Qin Zhang
Summary: In this note, the author demonstrates that the integral operator on Banach space AP is bounded or compact depending on whether the continuous function f belongs to the big Zygmund class ?* or the little Zygmund class lambda*. This finding generalizes previous research and serves as the infinitesimal version of another main result.
Article
Automation & Control Systems
Nicholas Sterge, Bharath K. Sriperumbudur
Summary: In this work, the trade-off between computational complexity and statistical accuracy in Nystro approximate kernel principal component analysis (KPCA) is theoretically studied. It is shown that Nystro approximate KPCA matches the statistical performance of (non-approximate) KPCA while remaining computationally beneficial. Additionally, Nystro approximate KPCA outperforms the statistical behavior of the random feature approximation when applied to KPCA.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Mathematics, Interdisciplinary Applications
Owen Huang, Sourav Saha, Jiachen Guo, Wing Kam Liu
Summary: Recent advances in operator learning theory have led to the development of a kernel learning method that can map between infinite dimensional spaces. However, the high training cost and lack of scalability of current deep learning methods pose challenges for large-scale engineering problems. This article provides a comprehensive analysis of the mathematical foundations of operator learning and proposes an algorithm to analytically approximate piecewise constant functions, suggesting the potential success of neural operators. Additionally, the article discusses the application of a kernel operator learning method for multiscale homogenization and presents preliminary results.
COMPUTATIONAL MECHANICS
(2023)
Article
Mathematics, Applied
Aigerim Kalybay, Ryskul Oinarov
Summary: In this paper, we establish necessary and sufficient conditions for weighted inequalities for a class of quasilinear integral operators with kernels.
BANACH JOURNAL OF MATHEMATICAL ANALYSIS
(2023)
Article
Engineering, Multidisciplinary
Arcady Beriozkin, Yishay Schlesinger
Summary: In this study, the relationship between two families of pairwise associated functions describing boundary wetting and drying curves is modeled. By using dependent domain theory of hysteresis, a linear integral operator is optimized to predict boundary drying curves from measured boundary wetting curves with the least sensitivity. The predictive performance of both operator models is good and can be enhanced by updating them with new incoming measured data.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Mathematics
Ming Xiong, Ao Yuan, Hong-Bin Fang, Colin O. Wu, Ming T. Tan
Summary: We develop a general framework for mean curve estimation in functional data analysis using RKHS and derive its asymptotic distribution theory. Two statistics for testing equality of mean curves and a mean curve belonging to a subspace are proposed. Simulation studies demonstrate the superior performance of the proposed method compared to existing ones.
Article
Computer Science, Artificial Intelligence
Zhaokun Wei, Xinlian Xie, Xiaoju Zhang
Summary: This paper presents a maritime anomaly detection algorithm based on a support vector machine (SVM) that considers the spatiotemporal and motion features of trajectories. A novel trajectory feature extraction method is proposed to accurately describe trajectory features. The algorithm recognizes vessel traffic patterns using density-based spatial clustering and detects anomalous behaviors using an improved SVM. A numerical example is provided to verify the effectiveness and accuracy of the proposed algorithm.
Article
Ecology
Zhuoran Yu, Christina L. Staudhammer, Sparkle L. Malone, Steven F. Oberbauer, Junbin Zhao, Julia A. Cherry, Gregory Starr
Summary: Wetlands are the largest natural source of methane, but the contribution of subtropical wetlands to global methane budgets is still unclear. This study compares methane fluxes from two freshwater marshes in the Florida Everglades and identifies seasonal patterns and biophysical drivers of methane emissions.
Article
Computer Science, Interdisciplinary Applications
Wonjun Ko, Wonsik Jung, Eunjin Jeon, Heung-Il Suk
Summary: This paper proposes a novel deep learning framework that effectively handles neuroimaging and genetic data simultaneously, achieving state-of-the-art performance in Alzheimer's disease and mild cognitive impairment identification. Unlike existing methods, the framework learns the relationship between imaging phenotypes and genotypes in a nonlinear way without prior neuroscientific knowledge.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Artificial Intelligence
Tomoharu Iwata, Motonobu Kanagawa, Tsutomu Hirao, Kenji Fukumizu
DATA MINING AND KNOWLEDGE DISCOVERY
(2017)
Article
Computer Science, Theory & Methods
Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
(2020)
Article
Computer Science, Theory & Methods
Daniel Andrade, Akiko Takeda, Kenji Fukumizu
STATISTICS AND COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu
Article
Geochemistry & Geophysics
Yuki Saito, Peng K. Hong, Takafumi Niihara, Hideaki Miyamoto, Kenji Fukumizu
METEORITICS & PLANETARY SCIENCE
(2020)
Article
Statistics & Probability
Daniel Andrade, Kenji Fukumizu
Article
Computer Science, Interdisciplinary Applications
Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2020)
Article
Computer Science, Artificial Intelligence
Daniel Andrade, Kenji Fukumizu, Yuzuru Okajima
Summary: This paper presents a covariate clustering method that takes into account sample class label information and formulates the problem as a convex optimization problem. Experimental results confirm the high utility and effectiveness of the proposed method.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Information Systems
Hironori Murase, Kenji Fukumizu
Summary: In this paper, we propose an Anomalous Latent variable Generative Adversarial Network (ALGAN) for anomaly detection tasks. It generates pseudo-anomalous data and fake-normal data, and the discriminator distinguishes between normal and pseudo-anomalous data. Experimental results showed that ALGAN performs comparably to state-of-the-art methods while achieving faster prediction time.
Proceedings Paper
Computer Science, Artificial Intelligence
Pengzhou (Abel) Wu, Kenji Fukumizu
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108
(2020)
Article
Biochemical Research Methods
Niko Yasui, Chrysafis Vogiatzis, Ruriko Yoshida, Kenji Fukumizu
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2020)
Article
Operations Research & Management Science
Ruriko Yoshida, Kenji Fukumizu, Chrysafis Vogiatzis
ANNALS OF OPERATIONS RESEARCH
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017)
(2017)
Article
Automation & Control Systems
Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka
JOURNAL OF MACHINE LEARNING RESEARCH
(2018)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.