Search Results - ML: Deep Generative Models & Autoencoders

Harnessing Generative Modeling and Autoencoders Against Adversarial Threats in Autonomous Vehicles by Raja, Kathiroli, Theerthagiri, Sudhakar, Swaminathan, Sriram Venkataraman, Suresh, Sivassri, Raja, Gunasekaran

“… To enable human behavior, Deep Learning (DL) and Machine Learning (ML) models are extensively used to make accurate decisions…”
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Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning by Rajasekar, Elakkiya, Chandra, Harshiv, Pears, Nick, Vairavasundaram, Subramaniyaswamy, Kotecha, Ketan

“…Proposed Architecture of Lung Image Diagnosis Using Generative Autoencoders in Unsupervised Ensemble Learning. [Display omitted] •Proposed GAME…”
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An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation by Alsafadi, Farah, Yaseen, Mahmoud, Wu, Xu

“… disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples…”
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At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence by Celik, Abdulkadir, Eltawil, Ahmed M.

“…-futuristic visions to life along with added technical intricacies. Although analytical models lay the foundations and offer systematic insights, we have recently witnessed a noticeable surge in research suggesting machine learning (ML…”
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Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation by Fuhr, Addis S., Sumpter, Bobby G.

“… Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent…”
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The emerging role of generative artificial intelligence in transplant medicine by Deeb, Maya, Gangadhar, Anirudh, Rabindranath, Madhumitha, Rao, Khyathi, Brudno, Michael, Sidhu, Aman, Wang, Bo, Bhat, Mamatha

“…Generative artificial intelligence (AI), a subset of machine learning that creates new content based on training data, has witnessed tremendous advances in recent years…”
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Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization by Zhou, Xiaokang, Zheng, Xuzhe, Shu, Tian, Liang, Wei, Wang, Kevin I-Kai, Qi, Lianyong, Shimizu, Shohei, Jin, Qun

“…) issue, in modern smart manufacturing or intelligent transportation systems (ITSs). In this study, we newly design and introduce a deep generative model framework, which seamlessly incorporates the information theoretic learning (ITL…”
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Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics by Mohan, Arvind T., Tretiak, Dima, Chertkov, Misha, Livescu, Daniel

“… of freedom required to resolve all the dynamically significant spatio-temporal scales. Designing efficient and accurate Machine Learning (ML…”
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Context-Aware Learning for Generative Models by Perdikis, Serafeim, Leeb, Robert, Chavarriaga, Ricardo, Millan, Jose del R.

“…This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables…”
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Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders by Yang, Hui, Aydi, Walid, Innab, Nisreen, Ghoneim, Mohamed E., Ferrara, Massimiliano

“… Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process…”
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An evolutionary variational autoencoder for perovskite discovery by Chenebuah, Ericsson Tetteh, Nganbe, Michel, Tchagang, Alain Beaudelaire

“… Previous efforts for simulating the discovery of novel perovskites via ML have often been limited to straightforward tabular-dataset models and compositional phase-field representations…”
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Deep generative learning for exploration in large electrochemical impedance dataset by Doonyapisut, Dulyawat, Kim, Byeongkyu, Kim, Jung Kyu, Lee, Eunseok, Chung, Chan-Hwa

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Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder by Oliveira, André F, Da Silva, Juarez L F, Quiles, Marcos G

“… Here we unite these applications under a single molecular representation and ML algorithm by modifying the grammar variational autoencoder (GVAE…”
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Deep Learning-Enhanced Gap Filling in Drosophila Melanogaster Genomic Data by Sharma, Jivitesh, Jetschny, Stefan, Kapun, Martin, Belaid, Mohamed B

“…This study introduces deep learning (DL) methods for imputing missing allele-frequency information in large-scale genome-wide pooled re-sequencing (Pool-Seq…”
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Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence by Dindorf, Carlo, Dully, Jonas, Konradi, Jürgen, Wolf, Claudia, Becker, Stephan, Simon, Steven, Huthwelker, Janine, Werthmann, Frederike, Kniepert, Johanna, Drees, Philipp, Betz, Ulrich, Fröhlich, Michael

“…Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets…”
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Mutated traffic detection and recovery: an adversarial generative deep learning approach by Salman, Ola, Elhajj, Imad H., Kayssi, Ayman, Chehab, Ali

“… In this paper, we propose a deep learning (DL) model to detect mutated traffic and recover the original one…”
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A survey of machine learning techniques in structural and multidisciplinary optimization by Ramu, Palaniappan, Thananjayan, Pugazhenthi, Acar, Erdem, Bayrak, Gamze, Park, Jeong Woo, Lee, Ikjin

“…Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years…”
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Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset by Wani, Aasim Ayaz, Abeer, Fatima

“…) and t-distributed stochastic neighbor embedding (t-SNE), and advanced imputation techniques including denoising autoencoders (DAE…”
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Imputation of Missing Values in Training Data using Variational Autoencoder by Hong, Xuerui, Hao, Shuang

“… The emergence of deep generative models also opens up new opportunities, especially for dealing with a particularly large number of missing values…”
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A feature mapping technique for complex data object generation with likelihood and deep generative approaches by Muramudalige, Shashika R., Jayasumana, Anura P., Wang, Haonan

“…When a sufficient amount of training data is available, Machine Learning (ML) models show great promise for solving problems involving complex and dynamic patterns…”
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