Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels by Osman Mamun, Madison Wenzlick, Arun Sathanur, Jeffrey Hawk, Ram Devanathan

“…In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.…”
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Speech Recognition for Task Domains with Sparse Matched Training Data by Byung Ok Kang, Hyeong Bae Jeon, Jeon Gue Park

“…For the active learning process, we designed an integrated system consisting of a variational autoencoder with an encoder that infers latent variables with disentangled attributes from the input speech, and a classifier that selects training data with attributes matching the target domain. …”
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Exact Equivariance, Disentanglement and Invariance of Transformations by Liao, Qianli, Poggio, Tomaso

“…Previous models like Variational Autoencoder [1] and Generative Adversarial Networks [2] attempted to learn disentangled representations from data with different levels of successes. …”
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Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks by Kim, Edward, Jensen, Zach, van Grootel, Alexander, Huang, Kevin, Staib, Matthew, Mysore, Sheshera, Chang, Haw-Shiuan, Strubell, Emma, McCallum, Andrew, Jegelka, Stefanie, Olivetti, Elsa

“…Starting from the natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for any inorganic materials of interest. …”
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Sequence to better sequence: Continuous revision of combinatorial structures by Jaakkola, Tommi, Gifford, David, Mueller, Jonas

“…To avoid combinatorial-search over sequence elements, we specify a generative model with continuous latent factors, which is learned via joint approximate inference using a recurrent variational autoencoder (VAE) and an outcome-predicting neural network module. …”
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EndoUDA: a modality independent segmentation approach for endoscopy imaging by Celik, N, Ali, S, Gupta, S, Braden, B, Rittscher, J

“…In this context, we propose a novel UDA-based segmentation method that couples the variational autoencoder and U-Net with a common EfficientNet-B4 backbone, and uses a joint loss for latent-space optimization for target samples. …”

Learning an expert skill-space for replanning dynamic quadruped locomotion over obstacles by Surovik, D, Melon, O, Geisert, M, Fallon, M, Havoutis, I

“…Motivated by the problem of quadrupedal locomotion over obstacles, we apply an approach that disentangles modal variation from task-to-solution regression by using a conditional variational autoencoder. The resulting decoder is a regression model that outputs trajectories based on the task and a real-valued latent mode vector representing a style of behavior. …”

Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture by Derraz, Radhwane, Muharam, Farrah Melissa, Jaafar, Noraini Ahmad, Yap, Ng Keng

“…This study aims to: (i) develop a trigonometric-Euclidean-smoother interpolation (TESI) for continuous time-series and non-time-series data augmentation to prevent DNNs from under/overfitting; (ii) compare the TESI performance to the tabular variational autoencoder (TVAE) and the conditional tabular generative adversarial network (CTGAN); and (iii) compare the DNN performance before and after data augmentation. …”

Accelerated discovery of eutectic compositionally complex alloys by generative machine learning by Z. Q. Chen, Y. H. Shang, X. D. Liu, Y. Yang

“…To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. …”
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CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation by Ming He, Boyang An, Jiwen Wang, Hao Wen

“…This paper presents counterfactual explanations by Considering temporal dependencies (CETD), a counterfactual explanation model that utilizes a variational autoencoder (VAE) for sequential recommendation and takes into account temporal dependencies. …”
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BERTIVITS: The Posterior Encoder Fusion of Pre-Trained Models and Residual Skip Connections for End-to-End Speech Synthesis by Zirui Wang, Minqi Song, Dongbo Zhou

“…The current state-of-the-art (SOTA) model, VITS, utilizes a conditional variational autoencoder architecture. However, it faces challenges, such as limited robustness, due to training solely on text and spectrum data from the training set. …”
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Utilizing B-Spline Curves and Neural Networks for Vehicle Trajectory Prediction in an Inverse Reinforcement Learning Framework by Mohammad Sadegh Jazayeri, Arash Jahangiri

“…A conditional variational autoencoder (CVAE) was used to generate candidate trajectories from these predicted coefficients. …”
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Comparative Analysis of Deep Learning Algorithm for Cancer Classification using Multi-omics Feature Selection by Nur Sabrina Azmi, Azurah A Samah, Vivekaanan Sirgunan, Zuraini Ali Shah, Hairudin Abdul Majid, Chan Weng Howe, Nies Hui Wen, Nuraina Syaza Azman

“…This study aims to investigate how deep learning algorithms, namely stacked denoising autoencoder (SDAE) and variational autoencoder (VAE) can be used in cancer classification using multi-omics data. …”
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Machine Learning Classifiers and Data Synthesis Techniques to Tackle with Highly Imbalanced COVID-19 Data by Avaz Naghipour, Mohammad Reza Abbaszadeh Bavil Soflaei, mostafa ghader-zefrehei

“…To this end, we demonstrate the capability of two advanced data synthesis algorithms, Conditional Tabular Generative Adversarial Network (CTGAN) and Tabular Variational Autoencoder (TVAE), in addressing the class imbalance inherent in the dataset. …”
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Uncertainty-Aware Reinforcement Learning for Portfolio Optimization by Bayaraa Enkhsaikhan, Ohyun Jo

“…This uncertainty is further addressed using a Variational Autoencoder (VAE) to estimate, and Cost Network to backpropogate riskiness through the model to learn actions with safe results. …”
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Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI by Lu Meng, Chuanhao Yang

“…Initially, we employ the Very Deep Variational Autoencoder (VDVAE) to reconstruct a coarse image from fMRI data, capturing the underlying details of the original image. …”
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Deep Learning-Based Path Planning Under Co-Safe Temporal Logic Specifications by Kyoungho Lee, Kyunghoon Cho

“…Initially, using a Conditional Variational Autoencoder (CVAE), the proposed approach efficiently identifies and encodes optimal trajectory distributions. …”
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Generative approaches for solving tangram puzzles by Fernanda Miyuki Yamada, Harlen Costa Batagelo, João Paulo Gois, Hiroki Takahashi

“…This paper investigates the application of four deep-learning architectures—Convolutional Autoencoder, Variational Autoencoder, U-Net, and Generative Adversarial Network—specifically designed for solving Tangram puzzles. …”
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Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder by Tianci Liu, Chun Wang, Gongjun Xu

“…To address this issue, we propose an importance-weighted sampling enhanced Variational Autoencoder (VAE) approach for the estimation of M3PL and M4PL. …”
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Identification of Sjögren’s syndrome patient subgroups by clustering of labial salivary gland DNA methylation profiles by Calvin Chi, Olivia Solomon, Caroline Shiboski, Kimberly E. Taylor, Hong Quach, Diana Quach, Lisa F. Barcellos, Lindsey A. Criswell

“…Specifically, hierarchical clustering was performed on low dimensional embeddings of DNA methylation data extracted from a variational autoencoder to uncover unknown heterogeneity. …”
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