Electromagnetic-Informed Generative Models for Passive RF Sensing and Perception of Body Motions by Stefano Savazzi, Federica Fieramosca, Sanaz Kianoush, Michele D'Amico, Vittorio Rampa

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Deep reinforcement learning navigation via decision transformer in autonomous driving by Lun Ge, Xiaoguang Zhou, Yongqiang Li, Yongcong Wang

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fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey by Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning

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Recent Trends in EEG-Based Motor Imagery Signal Analysis and Recognition: A Comprehensive Review by Neha Sharma, Manoj Sharma, Amit Singhal, Ritesh Vyas, Hasmat Malik, Asyraf Afthanorhan, Mohammad Asef Hossaini

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Goal-Driven Visual Question Generation from Radiology Images by Mourad Sarrouti, Asma Ben Abacha, Dina Demner-Fushman

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Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities by Amit Gangwal, Azim Ansari, Iqrar Ahmad, Abul Kalam Azad, Vinoth Kumarasamy, Vetriselvan Subramaniyan, Vetriselvan Subramaniyan, Ling Shing Wong

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Graph Laplacian Eigenvalues Empowered VAEs: A Novel Approach to Adaptive Latent Dimension Choice by M. K. Saravana, M. S. Roopa, J. S. Arunalatha, K. R. Venugopal

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Fusing Algorithms for Intersection of Computer Science and Art: Innovations in Generative Art and Interactive Digital Installations by Jingpeng Xie, Miaomiao Yu, Guangliang Liu

Subjects: “…Variational autoencoders (VAEs)…”
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High‐dimensional Bayesian optimization for metamaterial design by Zhichao Tian, Yang Yang, Sui Zhou, Tian Zhou, Ke Deng, Chunlin Ji, Yejun He, Jun S. Liu

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Exploration of Generative Neural Networks for Police Facial Sketches by Nerea Sádaba-Campo, Hilario Gómez-Moreno

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VAE-Driven Multimodal Fusion for Early Cardiac Disease Detection by Junxin Wang, Juanen Li, Rui Wang, Xinqi Zhou

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A systematic review of deep learning chemical language models in recent era by Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

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A tutorial review of mathematical techniques for quantifying tumor heterogeneity by Rebecca Everett, Kevin B. Flores, Nick Henscheid, John Lagergren, Kamila Larripa, Ding Li, John T. Nardini, Phuong T. T. Nguyen, E. Bruce Pitman, Erica M. Rutter

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Business Process Anomaly Detection and Root Cause Analysis Using BLSTM-VAE With Attention by Eman Abd El-Aziz, Radwa Fathalla, Yasser Ismail, Mohamed Shaheen

“…Combining these architectural components in the variational autoencoder architecture leads to learning high-level representations from the business process data and thus improving the reconstruction capability of the variational autoencoder. …”
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Learning to balance the coherence and diversity of response generation in generation-based chatbots by Shuliang Wang, Dapeng Li, Jing Geng, Longxing Yang, Hongyong Leng

“…In this article, we propose an improved method that improves the coherence and diversity of dialog generation by changing the model to use gamma sampling and adding attention mechanism to the knowledge-guided conditional variational autoencoder. The experimental results demonstrate that our proposed method can significantly improve the coherence and diversity of knowledge-guided conditional variational autoencoder for response generation in generation-based chatbots at the same time.…”
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VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data by Max Highsmith, Jianlin Cheng

“…Using a deep variational autoencoder, VEHiCLE provides a user tunable, full generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data across multiple metrics.…”
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A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems by Tala Talaei Khoei, Naima Kaabouch

“…The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models.…”
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Modeling the Uncertainty in Pedestrians Trajectory Prediction by Xiuhong Ma, Haitao Wang, Qiulin Ma

“…This paper proposes a spatiotemporal framework based on Transformer and conditional variational autoencoder (CVAE) models to predict pedestrian trajectories while accounting for uncertainty. …”
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Anomaly Detection Using Autoencoder With Feature Vector Frequency Map by Young-Gyu Kim, Tae-Hyoung Park

“…The prototype vector histogram is obtained from the vector quantized variational autoencoder’s codebook in the training stage. …”
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Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for Hyperspectral Classification by Zhitao Chen, Lei Tong, Bin Qian, Jing Yu, Chuangbai Xiao

“…To further improve the classification performance, based on the CVAEGAN, we propose a Self-Attention-Based Conditional Variational Autoencoder Generative Adversarial Network (SACVAEGAN). …”
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