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Relevant bibliographies by topics / Latent space smoothing

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Academic literature on the topic 'Latent space smoothing'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Latent space smoothing"

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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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Book chapters on the topic "Latent space smoothing"

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Peychev, Momchil, Anian Ruoss, Mislav Balunović, Maximilian Baader, and Martin Vechev. "Latent Space Smoothing for Individually Fair Representations." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19778-9_31.

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d’Invemo, Ray. "Relativistic cosmology." In Introducing Einstein’s Relativity. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198596530.003.0022.

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Abstract Cosmology is the study of the dynamical structure of the universe as a whole. As in most modelling exercises, we shall start by trying to find a very simple model of the universe. This is done by smoothing out all the irregularities in space and in time and concentrating simply on the gross features of the universe. So, to start with, we ignore all details such as the solar system, our own galaxy (the Milky Way), the local cluster of galaxies and so on; the consideration of these details can then hopefully be introduced at a later stage to yield a more complete or better theory. We sh

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Conference papers on the topic "Latent space smoothing"

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Liu, Yahui, Enver Sangineto, Yajing Chen, et al. "Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01064.

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Purwar, Anurag, Xiaoyi Chi, and Qiaode Jeffrey Ge. "Automatic Fairing of Two-Parameter Rational B-Spline Motion." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85458.

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This paper deals with the problem of automatic fairing of two-parameter B-Spline spherical and spatial motions. The concept of two-parameter freeform motions brings together the notion of the analytically determined two-parameter motions in Theoretical Kinematics and the concept of freeform surfaces in the field of Computer Aided Geometric Design (CAGD). A dual quaternion representation of spatial displacements is used and the problem of fairing two-parameter motions is studied as a surface fairing problem in the space of dual quaternions. By combining the latest results in surface fairing fro

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