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Journal articles on the topic 'Latent space smoothing'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Li, Yan, Xia Cai, Chunwei Wu, Xiao Lin, and Guitao Cao. "A Trustworthy Counterfactual Explanation Method With Latent Space Smoothing." IEEE Transactions on Image Processing 33 (2024): 4584–99. http://dx.doi.org/10.1109/tip.2024.3442614.

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Didier, Merk, Smit Denny, Beukers Boaz, and Mendsuren Tsatsral. "[Re] Reproducibility Study of "Latent Space Smoothing for Individually Fair Representations"." ReScience C 9, no. 2 (2023): #30. https://doi.org/10.5281/zenodo.8173725.

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Tamosiunas, Andrius, Hans A. Winther, Kazuya Koyama, David J. Bacon, Robert C. Nichol, and Ben Mawdsley. "Investigating cosmological GAN emulators using latent space interpolation." Monthly Notices of the Royal Astronomical Society 506, no. 2 (2021): 3049–67. http://dx.doi.org/10.1093/mnras/stab1879.

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ABSTRACT Generative adversarial networks (GANs) have been recently applied as a novel emulation technique for large-scale structure simulations. Recent results show that GANs can be used as a fast and efficient emulator for producing novel weak lensing convergence maps as well as cosmic web data in 2D and 3D. However, like any algorithm, the GAN approach comes with a set of limitations, such as an unstable training procedure, inherent randomness of the produced outputs, and difficulties when training the algorithm on multiple data sets. In this work, we employ a number of techniques commonly u

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Smith, Anne C., and Emery N. Brown. "Estimating a State-Space Model from Point Process Observations." Neural Computation 15, no. 5 (2003): 965–91. http://dx.doi.org/10.1162/089976603765202622.

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A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregress

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Fisher, Jacob C. "Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties." Sociological Methodology 49, no. 1 (2019): 258–94. http://dx.doi.org/10.1177/0081175018820075.

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Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model. In th

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Douglas, Jeff, Hae Rim Kim, Brian Habing, and Furong Gao. "Investigating Local Dependence With Conditional Covariance Functions." Journal of Educational and Behavioral Statistics 23, no. 2 (1998): 129–51. http://dx.doi.org/10.3102/10769986023002129.

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The local dependence of item pairs is investigated via a conditional covariance function estimation procedure. The conditioning variable used in the procedure is obtained by a monotonic transformation of total score on the remaining items. Intuitively, the conditioning variable corresponds to the unidimensional latent ability that is best measured by the test. The conditional covariance functions are estimated using kernel smoothing, and a standardization to adjust for the confounding effect of item difficulty is introduced. The particular standardization chosen is an adaptation of Yule’s coef

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Chen, Wei, Guo Ye, Yakun Wang, et al. "Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15875–83. https://doi.org/10.1609/aaai.v39i15.33743.

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Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs. Existing UGDA methods primarily focus on aligning features in the latent space learned by graph neural networks (GNNs) across domains, often overlooking structural shifts, resulting in limited effectiveness when addressing structurally complex transfer scenarios. Given the sensitivity of GNNs to local structural features, even slight discrepancies between source and target graphs could lead to significant shifts i

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Hu, Sile, Qiaosheng Zhang, Jing Wang, and Zhe Chen. "Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity." Journal of Neurophysiology 119, no. 4 (2018): 1394–410. http://dx.doi.org/10.1152/jn.00684.2017.

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Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have

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Lakshmanan, Karthik C., Patrick T. Sadtler, Elizabeth C. Tyler-Kabara, Aaron P. Batista, and Byron M. Yu. "Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays." Neural Computation 27, no. 9 (2015): 1825–56. http://dx.doi.org/10.1162/neco_a_00759.

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Noisy, high-dimensional time series observations can often be described by a set of low-dimensional latent variables. Commonly used methods to extract these latent variables typically assume instantaneous relationships between the latent and observed variables. In many physical systems, changes in the latent variables manifest as changes in the observed variables after time delays. Techniques that do not account for these delays can recover a larger number of latent variables than are present in the system, thereby making the latent representation more difficult to interpret. In this work, we

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Chen, Ning, Fuhai Hu, Jiayao Chen, Zhiwen Chen, Weihua Gui, and Xu Li. "A Process Monitoring Method Based on Dynamic Autoregressive Latent Variable Model and Its Application in the Sintering Process of Ternary Cathode Materials." Machines 9, no. 10 (2021): 229. http://dx.doi.org/10.3390/machines9100229.

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Due to the ubiquitous dynamics of industrial processes, the variable time lag raises great challenge to the high-precision industrial process monitoring. To this end, a process monitoring method based on the dynamic autoregressive latent variable model is proposed in this paper. First, from the perspective of process data, a dynamic autoregressive latent variable model (DALM) with process variables as input and quality variables as output is constructed to adapt to the variable time lag characteristic. In addition, a fusion Bayesian filtering, smoothing and expectation maximization algorithm i

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Song, Yu, Shan Lu, and Dehong Qiu. "Improving Node Classification through Convolutional Networks Built on Enhanced Message-Passing Graph." Computational Intelligence and Neuroscience 2022 (September 23, 2022): 1–16. http://dx.doi.org/10.1155/2022/3999144.

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Enhancing message propagation is critical for solving the problem of node classification in sparse graph with few labels. The recently popularized Graph Convolutional Network (GCN) lacks the ability to propagate messages effectively to distant nodes because of over-smoothing. Besides, the GCN with numerous trainable parameters suffers from overfitting when the labeled nodes are scarce. This article addresses the problem via building GCN on Enhanced Message-Passing Graph (EMPG). The key idea is that node classification can benefit from various variants of the input graph that can propagate mess

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Haug, Ola, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke. "Spatial trend analysis of gridded temperature data at varying spatial scales." Advances in Statistical Climatology, Meteorology and Oceanography 6, no. 1 (2020): 1–12. http://dx.doi.org/10.5194/ascmo-6-1-2020.

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Abstract. Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case

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Vrbanc, Filip, Mario Vašak, and Vinko Lešić. "Simple and Accurate Model of Thermal Storage with Phase Change Material Tailored for Model Predictive Control." Energies 16, no. 19 (2023): 6849. http://dx.doi.org/10.3390/en16196849.

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Thermal heat storage is becoming important in systems with renewable energy sources. Their largest benefit is smoothing the intermittent production and reduction in the site peak demand. The advantages of thermal energy storage with phase-change material are storing energy at a lower temperature for reduction in thermal losses, and enabling energy transfer at a constant temperature, which reduces the risk of equipment damage. In this paper, a low-order model of latent thermal energy storage, derived in a state-space form by using the mixed logical dynamical approach, is proposed. The model is

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Ibrahum, Ahmed Dawod Mohammed, Zhengyu Shang, and Jang-Eui Hong. "How Resilient Are Kolmogorov–Arnold Networks in Classification Tasks? A Robustness Investigation." Applied Sciences 14, no. 22 (2024): 10173. http://dx.doi.org/10.3390/app142210173.

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Kolmogorov–Arnold Networks (KANs) are a novel class of neural network architectures based on the Kolmogorov–Arnold representation theorem, which has demonstrated potential advantages in accuracy and interpretability over Multilayer Perceptron (MLP) models. This paper comprehensively evaluates the robustness of various KAN architectures—including KAN, KAN-Mixer, KANConv_KAN, and KANConv_MLP—against adversarial attacks, which constitute a critical aspect that has been underexplored in current research. We compare these models with MLP-based architectures such as MLP, MLP-Mixer, and ConvNet_MLP a

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Khakpash, Somayeh, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, and Charles Keeton. "Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps." Astrophysical Journal 980, no. 1 (2025): 35. https://doi.org/10.3847/1538-4357/ada5ff.

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Abstract Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extragalactic microlensing rely on computationally expensive magnification map generation. With large data sets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre

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Li, Liangwei, Lin Liu, Xiaohui Du, et al. "CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification." Sensors 22, no. 24 (2022): 9980. http://dx.doi.org/10.3390/s22249980.

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Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. H

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Wu, Yiqian, Hao Xu, Xiangjun Tang, et al. "Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior." ACM Transactions on Graphics 43, no. 4 (2024): 1–12. http://dx.doi.org/10.1145/3658162.

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Existing neural rendering-based text-to-3D-portrait generation methods typically make use of human geometry prior and diffusion models to obtain guidance. However, relying solely on geometry information introduces issues such as the Janus problem, over-saturation, and over-smoothing. We present Portrait3D , a novel neural rendering-based framework with a novel joint geometry-appearance prior to achieve text-to-3D-portrait generation that overcomes the aforementioned issues. To accomplish this, we train a 3D portrait generator, 3DPortraitGAN, as a robust prior. This generator is capable of prod

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Huang, Ko-Wei, Guan-Wei Chen, Zih-Hao Huang, and Shih-Hsiung Lee. "IWGAN: Anomaly Detection in Airport Based on Improved Wasserstein Generative Adversarial Network." Applied Sciences 13, no. 3 (2023): 1397. http://dx.doi.org/10.3390/app13031397.

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Anomaly detection is an important research topic in the field of artificial intelligence and visual scene understanding. The most significant challenge in real-world anomaly detection problems is the high imbalance of available data (i.e., non-anomalous versus anomalous data). This limits the use of supervised learning methods. Furthermore, the abnormal—and even normal—datasets in the airport field are relatively insufficient, causing them to be difficult to use to train deep neural networks when conducting experiments. Because generative adversarial networks (GANs) are able to effectively lea

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Lu, Jiawei, YingPeng Zhang, Zengjun Zhao, He Wang, Kun Zhou, and Tianjia Shao. "GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 5820–28. https://doi.org/10.1609/aaai.v39i6.32621.

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Large-scale text-guided image diffusion models have demonstrated remarkable results in text-to-image (T2I) generation. However, applying these models to synthesize textures for 3D geometries remains challenging due to the domain gap between 2D images and textures on a 3D surface. Early works that used a projecting-inpainting approach managed to preserve generation diversity, but often resulted in noticeable artifacts and style inconsistencies. While recent methods have attempted to address these inconsistencies, they often introduce other issues, such as blurring, over-saturation, or over-smoo

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Arab, Oussama, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, and David Rodríguez Collantes. "Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data." Applied Sciences 15, no. 12 (2025): 6614. https://doi.org/10.3390/app15126614.

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The reduction in disaster risk in urban regions due to natural hazards (e.g., earthquakes, landslides, floods, and tropical cyclones) is primarily a development matter that must be treated within the scope of a broader urban development framework. Natural hazard assessment is one of the turning points in mitigating disaster risk, which typically contributes to stronger urban resilience and more sustainable urban development. Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horiz

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Sang, Neil. "Does Time Smoothen Space? Implications for Space-Time Representation." ISPRS International Journal of Geo-Information 12, no. 3 (2023): 119. http://dx.doi.org/10.3390/ijgi12030119.

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The continuous nature of space and time is a fundamental tenet of many scientific endeavors. That digital representation imposes granularity is well recognized, but whether it is possible to address space completely remains unanswered. This paper argues Hales’ proof of Kepler’s conjecture on the packing of hard spheres suggests the answer to be “no”, providing examples of why this matters in GIS generally and considering implications for spatio-temporal GIS in particular. It seeks to resolve the dichotomy between continuous and granular space by showing how a continuous space may be emergent o

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Kieu, Quoc Viet, Vinh Nam Huynh, Thi Phuong Nghiem, Oanh Cuong Do, and Giang Son Tran. "A NEW METHOD FOR MEDICAL IMAGE FUSION BASED ON GAUSSIAN BLUR FILTER AND ROBINSON COMPASS OPERATOR." Journal of Computer Science and Cybernetics 40, no. 2 (2024): 135–46. http://dx.doi.org/10.15625/1813-9663/18655.

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Medical image fusion is a process of extracting features from multi-modal medical images and combining them into a composite image. It brings huge support in medical imaging and clinical diagnosis. However, the extraction of both structural and functional information from input MRI and PET images using multi-scale transform fusion methods poses a challenge of providing high-quality decomposition layers since during the decomposition process, images can still lose information such as blur or noise at the edges of the image. To address this limitation, we present a new method to improve the visu

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Wu, Xiaohan, Matthew McQuinn, Rahul Kannan та ін. "Imprints of temperature fluctuations on the z ∼ 5 Lyman-α forest: a view from radiation-hydrodynamic simulations of reionization". Monthly Notices of the Royal Astronomical Society 490, № 3 (2019): 3177–95. http://dx.doi.org/10.1093/mnras/stz2807.

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Abstract Reionization leads to large spatial fluctuations in the intergalactic temperature that can persist well after its completion. We study the imprints of such fluctuations on the $z$ ∼ 5 Ly α forest flux power spectrum using a set of radiation-hydrodynamic simulations that model different reionization scenarios. We find that large-scale coherent temperature fluctuations bring ${\sim}20\text{--}60{{\ \rm per\ cent}}$ extra power at k ∼ 0.002 s km−1, with the largest enhancements in the models where reionization is extended or ends the latest. On smaller scales (k ≳ 0.1 s km−1), we find th

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Baradaaji, A. "Joint Latent Space and Label Inference Estimation with Adaptive Fused Data and Label Graphs." ACM Transactions on Intelligent Systems and Technology, April 22, 2023. http://dx.doi.org/10.1145/3590172.

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Recently, structured computing has become an interesting topic in the world of artificial intelligence, especially in the field of machine learning, as most researchers focus on the development of graph-based semi-supervised learning models. In this paper, we present a new framework for graph-based semi-supervised learning. We present a powerful method for simultaneous label inference and linear transform estimation. The targeted linear transformation is used to obtain a discriminant subspace. To improve semi-supervised learning, our framework focuses on exploiting the data structure and soft

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Zhao, Bo-Wei, Xiao-Rui Su, Peng-Wei Hu, Yu-An Huang, Zhu-Hong You, and Lun Hu. "iGRLDTI: An Improved Graph Representation Learning Method for Predicting Drug-Target Interactions over Heterogeneous Biological Information Network." Bioinformatics, July 28, 2023. http://dx.doi.org/10.1093/bioinformatics/btad451.

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Abstract Motivation The task of predicting drug-target interactions (DTIs) plays a significant role in faciliating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from hetergeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the ove

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Koelle, Samson, Dana Mastrovito, Jennifer D. Whitesell, et al. "Modelling the cell-type specific mesoscale murine connectome with anterograde tracing experiments." Network Neuroscience, September 19, 2023, 1–28. http://dx.doi.org/10.1162/netn_a_00337.

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Abstract The Allen Mouse Brain Connectivity Atlas (MCA) consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class specific whole brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how m

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"Enhancing Satellite Imagery: A Novel Approach to Gaussian Noise Reduction Using Convolutional Neural Networks and Nonlinear Filtering Techniques." Iraqi Statisticians Journal, no. 2 (May 11, 2025): 43–51. https://doi.org/10.62933/37j8tb47.

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Image denoising is one of the fundamental aspects of removing noise from an image and enhancing its features containing visual information. Based on this, Convolutional Neural Networks (CNNs) have been a latest topic of study, with a wide range of applications in fields as diverse as diagnostic image denoising and low-light image denoising. In this paper, an image denoising method is proposed based on converting the noisy image to YUV colon space, extracting the noisy Y channel, and obtaining an appropriate smoothing parameter for the noisy Y channel using the cross-validation smoothing techni

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Burger, Pierre A., Lucas Porth, Sven Heydenreich, et al. "KiDS-1000 cosmology: Combined second- and third-order shear statistics." Astronomy & Astrophysics, December 22, 2023. http://dx.doi.org/10.1051/0004-6361/202347986.

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In this work, we perform the first cosmological parameter analysis of the fourth release of Kilo Degree Survey (KiDS-1000) data with second- and third-order shear statistics. This paper builds on a series of studies aimed at describing the roadmap to third-order shear statistics. We derived and tested a combined model of the second-order shear statistic, namely, the COSEBIs and the third-order aperture mass statistics MapMapMap in a tomographic set-up. We validated our pipeline with $N$-body mock simulations of the KiDS-1000 data release. To model the second- and third-order statistics, we use

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Perez-Panades, Jordi, Paloma Botella-Rocamora, and Miguel Angel Martinez-Beneito. "Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies." International Journal of Health Geographics 19, no. 1 (2020). http://dx.doi.org/10.1186/s12942-020-00251-z.

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Abstract Background Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more ep

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