Cooperative Advisory Residual Policies for Congestion Mitigation by Hasan, Aamir, Chakraborty, Neeloy, Chen, Haonan, Cho, Jung-Hoon, Wu, Cathy, Driggs-Campbell, Katherine

“…We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. …”
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Controllable music : supervised learning of disentangled representations for music generation by Watcharasupat, Karn N.

“…Specifically, we focus on controlling multiple continuous, potentially interdependent timbral attributes of a musical note using a variational autoencoder (VAE) framework, and the necessary groundwork research needed to support the goal. …”
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Machine learning for industrial IOT by Yeow, Brandon Wei Liang

“…This paper also explores data-level privacy techniques using generative models such as Variational Autoencoder. Visual quality of generated images shows little impact on the increase in accuracy. …”
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Global context with discrete diffusion in vector quantised modelling for image generation by Hu, Minghui, Wang, Yujie, Cham, Tat-Jen, Yang, Jianfei, Suganthan, Ponnuthurai Nagaratnam

“…The integration of Vector Quantised Variational AutoEncoder (VQ-VAE) with autoregressive models as generation part has yielded high-quality results on image generation. …”
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An ensemble method for investigating maritime casualties resulting in pollution occurrence: data augmentation and feature analysis by Li, Duowei, Wong, Yiik Diew, Chen, Tianyi, Wang, Nanxi, Yuen, Kum Fai

“…In the data preprocessing phase, key features related to casualties and vessels are extracted and encoded into model variables; in the data augmentation phase, Variational Autoencoder is employed to generate synthetic samples from the minor class, effectively mitigating the impact from data imbalance; and in the pollution indicator classification phase, machine learning models are trained on the balanced dataset to label a casualty as “polluting” or “non-polluting”. …”
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Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques by Martens, K

“…In this thesis, we build upon and propose extensions to two non-linear dimensionality reduction frameworks: the Gaussian Process Latent Variable Model (GPLVM) and the Variational Autoencoder (VAE). The former is based on Gaussian Processes, whereas the latter utilises neural networks. …”

Optimal transport based simulation methods for deep probabilistic models by Thornton, J

“…This has applications in differentiable sorting; clustering within the latent space of a variational autoencoder; and within particle filtering. The remaining two works contribute to the field of diffusion based generative modelling through the Schrödinger Bridge. …”

Machine learning based online traffic incident detection and management for urban networks by Yang, Huan

“…A Long Short Term Memory - Variational Autoencoder (LSTM-VAE) model is proposed to extract nonlinear features of traffic flow data with strong spatial-temporal correlations and build the incident-free model offline. …”
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Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias by Maarten Z. H. Kolk, Samuel Ruipérez-Campillo, Cornelis P. Allaart, Arthur A. M. Wilde, Reinoud E. Knops, Sanjiv M. Narayan, Fleur V. Y. Tjong, DEEP RISK investigators

“…Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. …”
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Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework by Shimei Qin, Shibin Sun, Yahui Wang, Chao Li, Lei Fu, Ming Wu, Jinxing Yan, Wan Li, Junjie Lv, Lina Chen

“…Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. …”
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StackVAE-G: An efficient and interpretable model for time series anomaly detection by Wenkai Li, Wenbo Hu, Ting Chen, Ning Chen, Cheng Feng

“…Combining these two modules, we introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph neural network) model for multivariate time series anomaly detection. …”
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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models by Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury

“…This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. …”
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Autoencoding a Soft Touch to Learn Grasping from On‐Land to Underwater by Ning Guo, Xudong Han, Xiaobo Liu, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Jiansheng Dai, Fang Wan, Chaoyang Song

“…This study investigates the transferability of grasping knowledge from on‐land to underwater via a vision‐based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high‐framerate camera captures the whole‐body deformations while a soft robotic finger interacts with physical objects on‐land and underwater. …”
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Learning Explainable Disentangled Representations of E-Commerce Data by Aligning Their Visual and Textual Attributes by Katrien Laenen, Marie-Francine Moens

“…Therefore, in this work, we design an explainable variational autoencoder framework (E-VAE) which leverages visual and textual item data to obtain disentangled item representations by jointly learning to disentangle the visual item data and to infer a two-level alignment of the visual and textual item data in a multimodal disentangled space. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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Hemisphere-Separated Cross-Connectome Aggregating Learning via VAE-GAN for Brain Structural Connectivity Synthesis by Qiankun Zuo, Hao Tian, Ruiheng Li, Jia Guo, Jianmin Hu, Long Tang, Yi Di, Heng Kong

“…Specifically, the latent representation is transformed from structural connectivity by the graph variational autoencoder (GVAE). To generate more diverse and high-quality structural connectivities, the hemisphere-separated generator with a cross-connectome aggregating mechanism is developed to first learn local topological patterns by splitting the whole brain into inter- and intra-hemispheres, then capture global topological characteristics among all the neighbors for each brain region. …”
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Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification by Shahab Eddin Jozdani, Brian Alan Johnson, Dongmei Chen

“…In this study, we carried out an experimental comparison among different architectures of DNNs (i.e., regular deep multilayer perceptron (MLP), regular autoencoder (RAE), sparse, autoencoder (SAE), variational autoencoder (AE), convolutional neural networks (CNN)), common ensemble algorithms (Random Forests (RF), Bagging Trees (BT), Gradient Boosting Trees (GB), and Extreme Gradient Boosting (XGB)), and SVM to investigate their potential for urban mapping using a GEOBIA approach. …”
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Semi-supervised active transfer learning for fetal ECG arrhythmia detection by Mohammad Reza Mohebbian, Hamid Reza Marateb, Khan A. Wahid

“…Then we used the unlabeled Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) of 26 subjects to fine-tune the trained model to fine-tune the trained model based on active learning to detect anomalies in binary form for fetal. A variational autoencoder is trained on all data (adult and fetal ECG), and clustering is applied to latent features extracted from data after dimension reduction. …”
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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. …”
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BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting by Duc Q. Nguyen, Nghia Q. Vo, Thinh T. Nguyen, Khuong Nguyen-An, Quang H. Nguyen, Dang N. Tran, Tho T. Quan

“…To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. …”
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