Rapid estimation of γ' solvus temperature for composition design of Ni-based superalloy via physics-informed generative artificial intelligence by Yunfei Ren, Tao Hu, Songzhe Xu, Chaoyue Chen, Weidong Xuan, Zhongming Ren

“…In the reverse design process, 20,000 virtual alloy samples were generated based on divide-and-conquer variational autoencoder which divides the dataset into distinct clusters by K-means algorithm provides a structured representation of the alloy composition space, thereby facilitating a more nuanced understanding of its inherent complexities. …”
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Optimized Block-Based Lossy Image Compression Technique for Wireless Sensor Networks by Bose A. Lungisani, Adamu M. Zungeru, Caspar K. Lebekwe, Abid Yahya

“…A comparison of our proposed algorithm with existing algorithms demonstrated that using a convolutional variational autoencoder and relative error-bound mechanism leads to a significant trade-off in distortion, compression ratio, and energy consumption in WSNs. …”
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A Deep-Learning Proteomic-Scale Approach for Drug Design by Brennan Overhoff, Zackary Falls, William Mangione, Ram Samudrala

“…We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. …”
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Evaluation of Synthetic Data Generation Techniques in the Domain of Anonymous Traffic Classification by Drake Cullen, James Halladay, Nathan Briner, Ram Basnet, Jeremy Bergen, Tenzin Doleck

“…This work compares the ability of a Conditional Tabular Generative Adversarial Network (CTGAN), Copula Generative Adversarial Network (CopulaGAN), Variational Autoencoder (VAE), and Synthetic Minority Over-sampling Technique (SMOTE) to create viable synthetic anonymous network traffic samples. …”
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UnCorrupt SMILES: a novel approach to de novo design by Linde Schoenmaker, Olivier J. M. Béquignon, Willem Jespers, Gerard J. P. van Westen

“…The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89%, with different models having different error-type distributions. …”
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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…The utilized techniques in this paper comprise the Multimodal Deep Boltzmann Machine (MDBM), Multimodal Variational Autoencoder (MVAE) and Attention-based Fusion Networks, all of which fully utilize the learned representations. …”
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The Analysis of Artificial Intelligence Digital Technology in Art Education Under the Internet of Things by Fulai Fang, Xiaohong Jiang

“…It is compared with Deep Convolutional Generative Adversarial Network (DCGAN) and Variational Autoencoder (VAE) models. The research results indicate that the designed IoT and GANs integrated system remarkably outperforms DCGAN and VAE in image generation quality, with an Inception Score of 4.5, which is more diverse and recognizable than other models. …”
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Leveraging Hyperspectral Remote Sensing Imaging for Agricultural Crop Classification Using Coot Bird Optimization With Entropy-Based Feature Fusion Model by Ibrahim M. Mehedi, Muhammad Bilal, Muhammad Shehzad Hanif, Thangam Palaniswamy, Mahendiran T. Vellingiri

“…Moreover, Deep Variational Autoencoder (DVAE) system was utilized for crop type classification process. …”
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Unsupervised learning architecture for classifying the transient noise of interferometric gravitational-wave detectors by Yusuke Sakai, Yousuke Itoh, Piljong Jung, Keiko Kokeyama, Chihiro Kozakai, Katsuko T. Nakahira, Shoichi Oshino, Yutaka Shikano, Hirotaka Takahashi, Takashi Uchiyama, Gen Ueshima, Tatsuki Washimi, Takahiro Yamamoto, Takaaki Yokozawa

“…In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. …”
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Explainable Model Fusion for Customer Journey Mapping by Kotaro Okazaki, Kotaro Okazaki, Katsumi Inoue, Katsumi Inoue

“…Following preprocessing to make latent variables and their dynamics transparent with latent Dirichlet allocation and a variational autoencoder, a post-hoc explanation is implemented in which a hidden Markov model and learning from an interpretation transition are combined with a long short-term memory architecture that learns sequential data between touchpoints for extracting attitude rules for CJM. …”
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Inferring Personalized and Race-Specific Causal Effects of Genomic Aberrations on Gleason Scores: A Deep Latent Variable Model by Zhong Chen, Andrea Edwards, Chindo Hicks, Kun Zhang, Kun Zhang

“…The core of the proposed model is a deep variational autoencoder (VAE) framework, which follows the causal structure of inference with proxies. …”
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GenAI-Based Models for NGSO Satellites Interference Detection by Almoatssimbillah Saifaldawla, Flor Ortiz, Eva Lagunas, Abuzar B. M. Adam, Symeon Chatzinotas

“…In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). …”
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Towards visual ego-motion learning in robots by Pillai, Sudeep, Leonard, John J

“…By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. …”
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Missing data imputation in a clinical registry with deep generative models by Dai, Wangzhi(Scientist in electrical engineering and computer science)Massachusetts Institute of Technology.

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Machine Learning-Augmented Spectroscopies for Intelligent Materials Design by Andrejevic, Nina

“…We illustrate the latter by training a variational autoencoder to retrieve the sample parameters of proximity-coupled heterostructures from their polarized neutron reflectometry profiles with high resolution. …”
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Deep Learning to Quantify Pulmonary Edema in Chest Radiographs by Horng, Steven, Liao, Ruizhi, Wang, Xin, Dalal, Sandeep, Golland, Polina, Berkowitz, Seth J

“…Deep learning models were developed using two approaches: a semisupervised model using a variational autoencoder and a pretrained supervised learning model using a dense neural network. …”
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Human tracking and path prediction for mobile robot navigation in crowded environment by Bhujel, Niraj

“…Secondly, the multi-model behavior is addressed using the Conditional Variational Autoencoder(cVAE) approach. A novel self-critical GatedGCN (SC-GCN) is proposed to learn social behaviors like collision avoidance and goal-reaching using the Actor-critic framework. …”
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Efficient out-of-distribution detection using latent space of β-VAE for cyber-physical systems by Ramakrishna, Shreyas, Rahiminasab, Zahra, Karsai, Gabor, Easwaran, Arvind, Easwaran, Arvind

“…Our contribution is an approach to design and train a single β-Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. …”
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AgileGAN: stylizing portraits by inversion-consistent transfer learning by Song, Guoxian, Luo, Linjie, Liu, Jing, Ma, Wan-Chun, Lai, Chunpong, Zheng, Chuanxia, Cham, Tat-Jen

“…We introduce a novel hierarchical variational autoencoder to ensure the inverse mapped distribution conforms to the original latent Gaussian distribution, while augmenting the original space to a multi-resolution latent space so as to better encode different levels of detail. …”
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Design methodology for deep out-of-distribution detectors in real-time cyber-physical systems by Yuhas, Michael, Ng, Daniel Jun Xian, Easwaran, Arvind

“…The methodology is demonstrated on two variational autoencoder based OOD detectors from the literature on two embedded platforms. …”
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