Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study av Byunggill Joe, Yonghyeon Park, Jihun Hamm, Insik Shin, Jiyeon Lee

“…To effectively avoid detection by manual inspectors, we employ variational autoencoders to learn the missing patterns in normal electronic health record data and produce trigger data that appears similar to this data. …”
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Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features. av Tomoki Sasagasako, Akihiko Ueda, Yohei Mineharu, Yusuke Mochizuki, Souichiro Doi, Silsu Park, Yukinori Terada, Noritaka Sano, Masahiro Tanji, Yoshiki Arakawa, Yasushi Okuno

“…<h4>Materials and methods</h4>Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. …”
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IF-TONIR: iteration-free topology optimization based on implicit neural representations av Hu, Jiangbei, He, Ying, Xu, Baixin, Wang, Shengfa, Lei, Na, Luo, Zhongxuan

“…IF-TONIR leverages Conditional Variational Autoencoders, which use a CNN-based encoder and a MLP-based decoder to learn and reconstruct optimal structures. …”
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A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z ∼ 8 av C. Tohill, S. P. Bamford, C. J. Conselice, L. Ferreira, T. Harvey, N. Adams, D. Austin

“…In this paper, we employ variational autoencoders to perform feature extraction on galaxies at z > 2 using JWST/NIRCam data. …”
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Fractional Wavelet-Based Generative Scattering Networks av Jiasong Wu, Jiasong Wu, Jiasong Wu, Jiasong Wu, Xiang Qiu, Xiang Qiu, Jing Zhang, Jing Zhang, Fuzhi Wu, Fuzhi Wu, Youyong Kong, Youyong Kong, Youyong Kong, Guanyu Yang, Guanyu Yang, Guanyu Yang, Lotfi Senhadji, Lotfi Senhadji, Huazhong Shu, Huazhong Shu, Huazhong Shu

“…Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. …”
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A new long-read mitochondrial-genome protocol (PacBio HiFi) for haemosporidian parasites: a tool for population and biodiversity studies av M. Andreína Pacheco, Axl S. Cepeda, Erica A. Miller, Scott Beckerman, Mitchell Oswald, Evan London, Nohra E. Mateus-Pinilla, Ananias A. Escalante

“…A pipeline (HmtG-PacBio Pipeline) to process the reads is also provided; it integrates multiple sequence alignments, a machine-learning algorithm that uses modified variational autoencoders, and a clustering method to identify the mitochondrial haplotypes/species in a sample. …”
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Variational Graph Convolutional Networks for Dynamic Graph Representation Learning av Aabid A. Mir, Megat F. Zuhairi, Shahrulniza Musa, Meshari H. Alanazi, Abdallah Namoun

“…To address the challenges, this research introduces V-GCN (Variational Graph Convolutional Network), a new model that integrates the probabilistic latent space modelling of Variational Autoencoders (VAEs) with the structural learning capabilities of Graph Convolutional Networks (GCNs). …”
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Latent Linear Adjustment Autoencoder v1.0: a novel method for estimating and emulating dynamic precipitation at high resolution av C. Heinze-Deml, S. Sippel, S. Sippel, A. G. Pendergrass, A. G. Pendergrass, A. G. Pendergrass, F. Lehner, F. Lehner, F. Lehner, N. Meinshausen

“…Building on variational autoencoders, we introduce a novel statistical model – the Latent Linear Adjustment Autoencoder (LLAAE) – that enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner. …”
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Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillatorResearch in context av Maarten Z.H. Kolk, Samuel Ruipérez-Campillo, Laura Alvarez-Florez, Brototo Deb, Erik J. Bekkers, Cornelis P. Allaart, Anne-Lotte C.J. Van Der Lingen, Paul Clopton, Ivana Išgum, Arthur A.M. Wilde, Reinoud E. Knops, Sanjiv M. Narayan, Fleur V.Y. Tjong

“…Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. …”
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Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human diseaseResearch in context av Vincent D. Friedrich, Peter Pennitz, Emanuel Wyler, Julia M. Adler, Dylan Postmus, Kristina Müller, Luiz Gustavo Teixeira Alves, Julia Prigann, Fabian Pott, Daria Vladimirova, Thomas Hoefler, Cengiz Goekeri, Markus Landthaler, Christine Goffinet, Antoine-Emmanuel Saliba, Markus Scholz, Martin Witzenrath, Jakob Trimpert, Holger Kirsten, Geraldine Nouailles

“…A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and patients with severe COVID-19, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. …”
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At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence av Abdulkadir Celik, Ahmed M. Eltawil

“…Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, autoregressive GMs, to name a few. …”
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Understanding Gaussian noise injections in neural networks av Camuto, A

“…However, using these methods at test-time can confer this sought out certifiable robustness, one which we study at length in the context of Variational Autoencoders, a class of deep probabilistic model which experience Gaussian noise injections by design.…”