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Journal articles on the topic 'Deep-Latent Variable Models'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Cheng, Debo, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, and Thuc Duy Le. "Causal Inference with Conditional Instruments Using Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7122–30. http://dx.doi.org/10.1609/aaai.v37i6.25869.

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The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practic

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Piriyakulkij, Wasu Top, Yingheng Wang, and Volodymyr Kuleshov. "Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 19921–30. https://doi.org/10.1609/aaai.v39i19.34194.

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We propose denoising diffusion variational inference (DDVI), a black-box variational inference algorithm for latent variable models which relies on diffusion models as flexible approximate posteriors. Specifically, our method introduces an expressive class of diffusion-based variational posteriors that perform iterative refinement in latent space; we train these posteriors with a novel regularized evidence lower bound (ELBO) on the marginal likelihood inspired by the wake-sleep algorithm. Our method is easy to implement (it fits a regularized extension of the ELBO), is compatible with black-bo

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Martínez-Palomera, Jorge, Joshua S. Bloom, and Ellianna S. Abrahams. "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters." Astronomical Journal 164, no. 6 (2022): 263. http://dx.doi.org/10.3847/1538-3881/ac9b3f.

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Abstract The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven model

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Nalisnick, Eric, Padhraic Smyth, and Dustin Tran. "A Brief Tour of Deep Learning from a Statistical Perspective." Annual Review of Statistics and Its Application 10, no. 1 (2023): 219–46. http://dx.doi.org/10.1146/annurev-statistics-032921-013738.

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We expose the statistical foundations of deep learning with the goal of facilitating conversation between the deep learning and statistics communities. We highlight core themes at the intersection; summarize key neural models, such as feedforward neural networks, sequential neural networks, and neural latent variable models; and link these ideas to their roots in probability and statistics. We also highlight research directions in deep learning where there are opportunities for statistical contributions.

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Wöber, Wilfried, Papius Tibihika, Cristina Olaverri-Monreal, Lars Mehnen, Peter Sykacek, and Harald Meimberg. "Comparison of Unsupervised Learning Methods for Natural Image Processing." Biodiversity Information Science and Standards 3 (July 4, 2019): e37886. https://doi.org/10.3897/biss.3.37886.

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For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object detection (Ren et al. 2015) or pixel-wise weed classification (Milioto et al. 2017) machine learning is used for both feature extraction and processing (e.g. classification or regression). Historically, feature extraction (e.g. PCA; Ch. 12.1. in Bishop 2006) and processing were sequential and independent tasks (Wöber et al. 2013). Since the rise of convolutional neuronal networks (LeCun et al. 1989), a deep machine learning approach optimized for images, in 2012 (Krizhevsky et al. 2012), feature

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Yang, Zeyu, and Zhiqiang Ge. "Monitoring and prediction of big process data with deep latent variable models and parallel computing." Journal of Process Control 92 (August 2020): 19–34. http://dx.doi.org/10.1016/j.jprocont.2020.05.010.

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Shen, Bingbing, Le Yao, and Zhiqiang Ge. "Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure." Control Engineering Practice 94 (January 2020): 104198. http://dx.doi.org/10.1016/j.conengprac.2019.104198.

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Wöber, Wilfried, Lars Mehnen, Manuel Curto, Papius Dias Tibihika, Genanaw Tesfaye, and Harald Meimberg. "Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning." Applied Sciences 12, no. 6 (2022): 3158. http://dx.doi.org/10.3390/app12063158.

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The biological investigation of a population’s shape diversity using digital images is typically reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compar

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Trieu, Hai-Long, Makoto Miwa, and Sophia Ananiadou. "BioVAE: a pre-trained latent variable language model for biomedical text mining." Bioinformatics 38, no. 3 (2021): 872–74. http://dx.doi.org/10.1093/bioinformatics/btab702.

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Abstract Summary Large-scale pre-trained language models (PLMs) have advanced state-of-the-art (SOTA) performance on various biomedical text mining tasks. The power of such PLMs can be combined with the advantages of deep generative models. These are examples of these combinations. However, they are trained only on general domain text, and biomedical models are still missing. In this work, we describe BioVAE, the first large-scale pre-trained latent variable language model for the biomedical domain, which uses the OPTIMUS framework to train on large volumes of biomedical text. The model shows

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Cofre-Martel, Sergio, Enrique Lopez Droguett, and Mohammad Modarres. "Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables." Shock and Vibration 2021 (May 27, 2021): 1–15. http://dx.doi.org/10.1155/2021/9937846.

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Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of phy

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Kim, Ha Young, and Dongsup Kim. "Prediction of mutation effects using a deep temporal convolutional network." Bioinformatics 36, no. 7 (2019): 2047–52. http://dx.doi.org/10.1093/bioinformatics/btz873.

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Abstract Motivation Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in

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Toledo-Marín, J. Quetzalcóatl, and James A. Glazier. "Using deep LSD to build operators in GANs latent space with meaning in real space." PLOS ONE 18, no. 6 (2023): e0287736. http://dx.doi.org/10.1371/journal.pone.0287736.

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Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent spac

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Alam, Fardina Fathmiul, and Amarda Shehu. "Data Size and Quality Matter: Generating Physically-Realistic Distance Maps of Protein Tertiary Structures." Biomolecules 12, no. 7 (2022): 908. http://dx.doi.org/10.3390/biom12070908.

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With the debut of AlphaFold2, we now can get a highly-accurate view of a reasonable equilibrium tertiary structure of a protein molecule. Yet, a single-structure view is insufficient and does not account for the high structural plasticity of protein molecules. Obtaining a multi-structure view of a protein molecule continues to be an outstanding challenge in computational structural biology. In tandem with methods formulated under the umbrella of stochastic optimization, we are now seeing rapid advances in the capabilities of methods based on deep learning. In recent work, we advance the capabi

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Sun, Jiankai, Rui Liu, and Bolei Zhou. "HiABP: Hierarchical Initialized ABP for Unsupervised Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9747–55. http://dx.doi.org/10.1609/aaai.v35i11.17172.

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Although Markov chain Monte Carlo (MCMC) is useful for generating samples from the posterior distribution, it often suffers from intractability when dealing with large-scale datasets. To address this issue, we propose Hierarchical Initialized Alternating Back-propagation (HiABP) for efficient Bayesian inference. Especially, we endow Alternating Backpropagation (ABP) method with a well-designed initializer and hierarchical structure, composing the pipeline of Initializing, Improving, and Learning back-propagation. It saves much time for the generative model to initialize the latent variable by

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Wöber, Wilfried, Lars Mehnen, Peter Sykacek, and Harald Meimberg. "Investigating Explanatory Factors of Machine Learning Models for Plant Classification." Plants 10, no. 12 (2021): 2674. http://dx.doi.org/10.3390/plants10122674.

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Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learn

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Woo, Min Sun, Eunhak Lee, Woobin Lee, and Seongyong Kim. "Comparison of variational autoencoders for anomaly detection of electrocardiogram." Korean Data Analysis Society 26, no. 2 (2024): 489–99. http://dx.doi.org/10.37727/jkdas.2024.26.2.489.

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Electrocardiogram is widely used as a tool for detecting and diagnosing heart disease. Previous research revealed that deep learning models based on supervised learning have limited performance due to imbalance between normal and anomaly. To overcome this problem, anomaly detection models using variational autoencoder (VAE) have been proposed. After training VAE model using normal data, a threshold to distinguish normal and anomaly is set by using validation data, then test data is applied. VAE model has encoder, a latent variable layer, and decoder. and deep learning models are embedded in en

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Anumasa, Srinivas, and P. K. Srijith. "Latent Time Neural Ordinary Differential Equations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6010–18. http://dx.doi.org/10.1609/aaai.v36i6.20547.

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Neural ordinary differential equations (NODE) have been proposed as a continuous depth generalization to popular deep learning models such as Residual networks (ResNets). They provide parameter efficiency and automate the model selection process in deep learning models to some extent. However, they lack the much-required uncertainty modelling and robustness capabilities which are crucial for their use in several real-world applications such as autonomous driving and healthcare. We propose a novel and unique approach to model uncertainty in NODE by considering a distribution over the end-time T

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Grachten, Maarten, Stefan Lattner, and Emmanuel Deruty. "BassNet: A Variational Gated Autoencoder for Conditional Generation of Bass Guitar Tracks with Learned Interactive Control." Applied Sciences 10, no. 18 (2020): 6627. http://dx.doi.org/10.3390/app10186627.

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Deep learning has given AI-based methods for music creation a boost by over the past years. An important challenge in this field is to balance user control and autonomy in music generation systems. In this work, we present BassNet, a deep learning model for generating bass guitar tracks based on musical source material. An innovative aspect of our work is that the model is trained to learn a temporally stable two-dimensional latent space variable that offers interactive user control. We empirically show that the model can disentangle bass patterns that require sensitivity to harmony, instrumen

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Zhu, Zifan, Yingying Fan, Yinfei Kong, Jinchi Lv, and Fengzhu Sun. "DeepLINK: Deep learning inference using knockoffs with applications to genomics." Proceedings of the National Academy of Sciences 118, no. 36 (2021): e2104683118. http://dx.doi.org/10.1073/pnas.2104683118.

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We propose a deep learning–based knockoffs inference framework, DeepLINK, that guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is applicable to a broad class of covariate distributions described by the possibly nonlinear latent factor models. It consists of two major parts: an autoencoder network for the knockoff variable construction and a multilayer perceptron network for feature selection with the FDR control. The empirical performance of DeepLINK is investigated through extensive simulation studies, where it is shown to achieve FDR control in featur

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Heinze-Deml, Christina, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen. "Latent Linear Adjustment Autoencoder v1.0: a novel method for estimating and emulating dynamic precipitation at high resolution." Geoscientific Model Development 14, no. 8 (2021): 4977–99. http://dx.doi.org/10.5194/gmd-14-4977-2021.

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Abstract. A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challen

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Touloupas, Konstantinos, and Paul Peter Sotiriadis. "Mixed-Variable Bayesian Optimization for Analog Circuit Sizing through Device Representation Learning." Electronics 11, no. 19 (2022): 3127. http://dx.doi.org/10.3390/electronics11193127.

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In this work, a deep representation learning method is proposed to build continuous-valued representations of individual integrated circuit (IC) devices. These representations are used to render mixed-variable analog circuit sizing problems as continuous ones and to apply a low-budget black box Bayesian optimization (BO) variant to solve them. By transforming the initial search spaces into continuous-valued ones, the BO’s Gaussian process models (GPs), which typically operate on real-valued spaces, can be used to guide the optimization search towards the global optimum. The proposed Device Rep

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Xiang, Gang, and Kun Tian. "Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning." International Journal of Aerospace Engineering 2021 (October 13, 2021): 1–16. http://dx.doi.org/10.1155/2021/6099818.

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In recent years, deep learning methods which promote the accuracy and efficiency of fault diagnosis task without any extra requirement of artificial feature extraction have elicited the attention of researchers in the field of manufacturing industry as well as aerospace. However, the problems that data in source and target domains usually have different probability distributions because of different working conditions and there are insufficient labeled or even unlabeled data in target domain significantly deteriorate the performance and generalization of deep fault diagnosis models. To address

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Zhu, Xianglin, Khalil Ur Rehman, Bo Wang, and Muhammad Shahzad. "Modern Soft-Sensing Modeling Methods for Fermentation Processes." Sensors 20, no. 6 (2020): 1771. http://dx.doi.org/10.3390/s20061771.

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For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling method

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Komorska, Iwona, and Andrzej Puchalski. "Condition Monitoring Using a Latent Space of Variational Autoencoder Trained Only on a Healthy Machine." Sensors 24, no. 21 (2024): 6825. http://dx.doi.org/10.3390/s24216825.

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Machine learning generative models have opened up a new perspective for automated machine diagnostics. These methods improve decision-making by extracting features, classifying, and creating new observations using deep neural networks. Generative modeling aims to determine the joint distribution of input data. This contrasts traditional methods used in diagnostics based on discriminative models and the conditional probability distribution of the target variable at known feature values. In the variational autoencoder (VAE) algorithms trained by the authors, the parameters of diagnostic features

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Ji, Shulei, and Xinyu Yang. "MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12821–29. http://dx.doi.org/10.1609/aaai.v38i11.29178.

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Generating music with emotion is an important task in automatic music generation, in which emotion is evoked through a variety of musical elements (such as pitch and duration) that change over time and collaborate with each other. However, prior research on deep learning-based emotional music generation has rarely explored the contribution of different musical elements to emotions, let alone the deliberate manipulation of these elements to alter the emotion of music, which is not conducive to fine-grained element-level control over emotions. To address this gap, we present a novel approach emp

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Perry, R. Ian, Kelly Young, Moira Galbraith, Peter Chandler, Antonio Velez-Espino, and Steve Baillie. "Zooplankton variability in the Strait of Georgia, Canada, and relationships with the marine survivals of Chinook and Coho salmon." PLOS ONE 16, no. 1 (2021): e0245941. http://dx.doi.org/10.1371/journal.pone.0245941.

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The Strait of Georgia, Canada, has complex interactions among natural and human pressures that confound understanding of changes in this system. We report on the interannual variability in biomass of 12 zooplankton taxonomic groups in the deep (bottom depths greater than 50 m) central and northern Strait of Georgia from 1996 to 2018, and their relationships with 10 physical variables. Total zooplankton biomass was dominated (76%) by large-sized crustaceans (euphausiids, large and medium size calanoid copepods, amphipods). The annual anomaly of total zooplankton biomass was highest in the late

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Han, Bingyuan, Peiyan Duan, Chengcheng Zhou, et al. "Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network." Plants 13, no. 12 (2024): 1681. http://dx.doi.org/10.3390/plants13121681.

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In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model’s capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results de

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Zhao, Huan, Tingting Li, Yufeng Xiao, and Yu Wang. "Improving Multi-Agent Generative Adversarial Nets with Variational Latent Representation." Entropy 22, no. 9 (2020): 1055. http://dx.doi.org/10.3390/e22091055.

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Generative adversarial networks (GANs), which are a promising type of deep generative network, have recently drawn considerable attention and made impressive progress. However, GAN models suffer from the well-known problem of mode collapse. This study focuses on this challenge and introduces a new model design, called the encoded multi-agent generative adversarial network (E-MGAN), which tackles the mode collapse problem by introducing the variational latent representations learned from a variable auto-encoder (VAE) to a multi-agent GAN. The variational latent representations are extracted fro

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Brunke, Michael A., Patrick Broxton, Jon Pelletier, et al. "Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5)." Journal of Climate 29, no. 9 (2016): 3441–61. http://dx.doi.org/10.1175/jcli-d-15-0307.1.

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Abstract One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.9° latitude × 1.25° longitude grid cell. The greatest changes in the simulation

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Kang, Zhiqiao. "Exploring the Effectiveness of Hyperparameters in Deep Convolution Generative Adversarial Networks." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 178–88. http://dx.doi.org/10.62051/h3sxs218.

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Traditional machine learning models are often only able to classify or predict data, and cannot generate new simulated data, while Generative Adversarial Network (GAN) provides the possibility for machines to generate high-quality and diverse data. GAN can be used for image data generation, image-to-image transformation, translation of image information and text information into each other, and so on. There are many different types of GAN can realize different types of functions. GAN has a strong generating ability, can generate some high-quality simulation data for human reference. The framew

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Bandeen-Roche, Karen. "SIGNAL DETECTION AND VALIDATION IN AN ERA OF BIG GERONTOLOGICAL DATA." Innovation in Aging 6, Supplement_1 (2022): 178. http://dx.doi.org/10.1093/geroni/igac059.712.

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Abstract Older adult health assessment long has posed measurement challenges—multidimensionality of sentinel outcomes like functioning and frailty, for example. This presentation discusses three developments creating opportunities for gerontologic biostatistics (GBS) over the past 10 years. Firstly, modeling to internally validate measurements or to quantify systematic heterogeneity in assessing older adult health has become considerably more widespread. Confirmatory latent variable modeling, harmonization, and mixture models will be addressed. Secondly, signal intensive behavioral phenotypes

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Guimard, Quentin, Lucile Sassatelli, Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, and Bimbo Alberto Del. "Deep Variational Learning for 360° Adaptive Streaming." ACM Transactions on Multimedia Computing, Communications and Applications 20, no. 9 (2024): 263. https://doi.org/10.1145/3643031.

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Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple h

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Guimard, Quentin, Lucile Sassatelli, Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, and Bimbo Alberto Del. "Deep Variational Learning for 360° Adaptive Streaming." ACM Transactions on Multimedia Computing, Communications and Applications 20, no. 9 (2024): 263. https://doi.org/10.1145/3643031.

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Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple h

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Guimard, Quentin, Lucile Sassatelli, Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, and Bimbo Alberto Del. "Deep Variational Learning for 360° Adaptive Streaming." ACM Transactions on Multimedia Computing, Communications and Applications 20, no. 9 (2024): 263. https://doi.org/10.1145/3643031.

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Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple h

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Guimard, Quentin, Lucile Sassatelli, Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, and Bimbo Alberto Del. "Deep Variational Learning for 360° Adaptive Streaming." ACM Transactions on Multimedia Computing, Communications and Applications 20, no. 9 (2024): 263. https://doi.org/10.1145/3643031.

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Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple h

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Guimard, Quentin, Lucile Sassatelli, Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, and Bimbo Alberto Del. "Deep Variational Learning for 360° Adaptive Streaming." ACM Transactions on Multimedia Computing, Communications and Applications 20, no. 9 (2024): 263. https://doi.org/10.1145/3643031.

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Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple h

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Jasmin, Praful Bharadiya. "Exploring the Use of Recurrent Neural Networks for Time Series Forecasting." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 2023–27. https://doi.org/10.5281/zenodo.8020803.

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Recurrent Neural Networks (RNNs) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as exponential smoothing (ETS) and the autoregressive integrated moving average (ARIMA) gain their popularity not only from their high accuracy, but also because they are suitable for non-expert users in that they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN archite

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Gaikwad, Akshay V., and Suyash Awate. "Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations." Machine Learning for Biomedical Imaging 2, June 2024 (2024): 717–60. http://dx.doi.org/10.59275/j.melba.2024-2fgd.

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Typical methods for semantic image segmentation rely on large training sets comprising per-pixel semantic segmentations. In medical-imaging applications, obtaining a large number of expert segmentations can be difficult because of the underlying demands on the experts’ time and the budget. However, in many such applications, it is much easier to obtain image-level information indicating the class labels of the objects of interest present in the image. We propose a novel deep-neural-network (DNN) framework for the semantic segmentation of images relying on weakly-and-semi-supervised learning fr

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Chunqi, Du, and Shinobu Hasegawa. "3dinfogan: 3d Models’ Reconstruction In Infogans." Asia-Pacific Journal of Information Technology and Multimedia 10, no. 02 (2021): 95–109. http://dx.doi.org/10.17576/apjitm-2021-1002-07.

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In computer vision and computer graphics, 3D reconstruction is the process of capturing real objects’ shapes and appearances. 3D models always can be constructed by active methods which use high-quality scanner equipment, or passive methods that learn from the dataset. However, both of these two methods only aimed to construct the 3D models, without showing what element affects the generation of 3D models. Therefore, the goal of this research is to apply deep learning to automatically generating 3D models, and finding the latent variables which affect the reconstructing process. The existing r

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Minervini, Pasquale, Luca Franceschi, and Mathias Niepert. "Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9200–9208. http://dx.doi.org/10.1609/aaai.v37i8.26103.

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The integration of discrete algorithmic components in deep learning architectures has numerous applications. Recently, Implicit Maximum Likelihood Estimation, a class of gradient estimators for discrete exponential family distributions, was proposed by combining implicit differentiation through perturbation with the path-wise gradient estimator. However, due to the finite difference approximation of the gradients, it is especially sensitive to the choice of the finite difference step size, which needs to be specified by the user. In this work, we present Adaptive IMLE (AIMLE), the first adapti

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Eiximeno, Benet, Arnau Miró, Ivette Rodríguez, and Oriol Lehmkuhl. "Toward the Usage of Deep Learning Surrogate Models in Ground Vehicle Aerodynamics." Mathematics 12, no. 7 (2024): 998. http://dx.doi.org/10.3390/math12070998.

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This study introduces a deep learning surrogate model designed to predict the evolution of the mean pressure coefficient on the back face of a Windsor body across a range of yaw angles from 2.5∘ to 10∘. Utilizing a variational autoencoder (VAE), the model effectively compresses snapshots of back pressure taken at yaw angles of 2.5∘, 5∘, and 10∘ into two latent vectors. These snapshots are derived from wall-modeled large eddy simulations (WMLESs) conducted at a Reynolds number of ReL=2.9×106. The frequencies that dominate the latent vectors correspond closely with those observed in both the dra

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Gou, Zixing, Yifan Sun, Zhebin Jin, Hanqiu Hu, and Weiyi Xia. "Label noise learning with the combination of CausalNL and CGAN models." Applied and Computational Engineering 79, no. 1 (2024): 97–106. http://dx.doi.org/10.54254/2755-2721/79/20241399.

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Since Deep Neural Networks easily overfit label errors, which will degenerate the performance of Deep Learning algorithms, recent research gives a lot of methodology for this problem. A recent model, causalNL, uses a structural causalNL model for instance-dependent label-noise learning and obtained excellent experimental results. The implementation of the algorithm is based on the VAE model, which encodes latent variables Y and Z with the observable variables X and Y. This in turn generates the transfer matrix. But it relies on some unreasonable assumptions. In this paper, we introduce CGAN to

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Carpenter, Chris. "Integrated Deep-Learning and Physics-Based Models Improve Production Prediction." Journal of Petroleum Technology 74, no. 11 (2022): 78–80. http://dx.doi.org/10.2118/1122-0078-jpt.

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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204864, “Integrating Deep-Learning and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs,” by Syamil M. Razak, SPE, Jodel Cornelio, SPE, and Atefeh Jahandideh, SPE, University of Southern California, et al. The paper has not been peer reviewed. _ The physics of fluid flow and transport processes in hydraulically fractured unconventional reservoirs is not well understood. As a result, predicted production behavior using conventional simulation often does not agre

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Kohjima, Masahiro. "Shuffled Deep Regression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13238–45. http://dx.doi.org/10.1609/aaai.v38i12.29224.

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Shuffled regression is the problem of learning regression models from shuffled data that consists of a set of input features and a set of target outputs where the correspondence between the input and output is unknown. This study proposes a new deep learning method for shuffled regression called Shuffled Deep Regression (SDR). We derive the sparse and stochastic variant of the Expectation-Maximization algorithm for SDR that iteratively updates discrete latent variables and the parameters of neural networks. The effectiveness of the proposal is confirmed by benchmark data experiments.

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Zhu, Weijin, Yao Shen, Mingqian Liu, and Lizeth Patricia Aguirre Sanchez. "GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture Model." Computational Intelligence and Neuroscience 2022 (July 18, 2022): 1–13. http://dx.doi.org/10.1155/2022/7254462.

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Recent studies on unsupervised object detection based on spatial attention have achieved promising results. Models, such as AIR and SPAIR, output “what” and “where” latent variables that represent the attributes and locations of objects in a scene, respectively. Most of the previous studies concentrate on the “where” localization performance. However, we claim that acquiring “what” object attributes is also essential for representation learning. This study presents a framework, GMAIR, for unsupervised object detection. It incorporates spatial attention and a Gaussian mixture in a unified deep

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Petkus, Andrew, Xinhui Wang, Susan Resnick, et al. "AFFECTIVE TRAJECTORIES: RISK OF DEMENTIA AND UNDERLYING STRUCTURAL BRAIN VARIABLES IN OLDER WOMEN." Innovation in Aging 6, Supplement_1 (2022): 230. http://dx.doi.org/10.1093/geroni/igac059.913.

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Abstract Understanding how trajectories of positive and negative affect relate to dementia risk and underlying structural brain variables is important for dementia prevention. We examined associations between annually assessed Positive and Negative Affect Scale subscales and dementia risk (2000-18) among cognitively-intact community-dwelling women (N=948; aged 72.9±3.7) from the Women’s Health Initiative Study of Cognitive Aging (years 2000-2010) and Magnetic Resonance Imaging Study (2005-2006). Joint latent class mixture models were constructed to identify latent classes of women with similar

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Beguš, Gašper. "Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication." Transactions of the Association for Computational Linguistics 9 (2021): 1180–96. http://dx.doi.org/10.1162/tacl_a_00421.

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Abstract This paper models unsupervised learning of an identity-based pattern (or copying) in speech called reduplication from raw continuous data with deep convolutional neural networks. We use the ciwGAN architecture (Beguš, 2021a) in which learning of meaningful representations in speech emerges from a requirement that the CNNs generate informative data. We propose a technique to wug-test CNNs trained on speech and, based on four generative tests, argue that the network learns to represent an identity-based pattern in its latent space. By manipulating only two categorical variables in the l

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Mosser, Lukas, Olivier Dubrule, and Martin J. Blunt. "Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks as a Geological Prior." Mathematical Geosciences 52, no. 1 (2019): 53–79. http://dx.doi.org/10.1007/s11004-019-09832-6.

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AbstractWe present an application of deep generative models in the context of partial differential equation constrained inverse problems. We combine a generative adversarial network representing an a priori model that generates geological heterogeneities and their petrophysical properties, with the numerical solution of the partial-differential equation governing the propagation of acoustic waves within the earth’s interior. We perform Bayesian inversion using an approximate Metropolis-adjusted Langevin algorithm to sample from the posterior distribution of earth models given seismic observati

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Shimaoka, Nanako, Shogo Okamoto, Yasuhiro Akiyama, and Yoji Yamada. "Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis." Foods 11, no. 6 (2022): 781. http://dx.doi.org/10.3390/foods11060781.

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Sensory responses dynamically change while eating foods. Temporal dominance of sensations (TDS) methods record temporal evolution and have attracted attention in the last decade. ISO 13299 recommends that different levels of attributes are investigated in separate TDS trials. However, only a few studies have attempted to link the dynamics of two different levels of sensory attributes. We propose a method to link the concurrent values of dominance proportions for primary- and multi-sensory attributes using canonical correlation analysis. First, panels categorized several attributes into primary

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Zhu, Xinqi, Chang Xu, and Dacheng Tao. "ContraFeat: Contrasting Deep Features for Semantic Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11470–78. http://dx.doi.org/10.1609/aaai.v37i9.26356.

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StyleGAN has shown strong potential for disentangled semantic control, thanks to its special design of multi-layer intermediate latent variables. However, existing semantic discovery methods on StyleGAN rely on manual selection of modified latent layers to obtain satisfactory manipulation results, which is tedious and demanding. In this paper, we propose a model that automates this process and achieves state-of-the-art semantic discovery performance. The model consists of an attention-equipped navigator module and losses contrasting deep-feature changes. We propose two model variants, with one

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