Results 11 to 20 of about 3,896 (184)
AE-Flow: Autoencoder Normalizing Flow
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, without the need for parallel data.
Jakub Mosinski +4 more
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Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising
IEEE Transactions on Medical Imaging, 2020 Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron.
Fenglei Fan +8 more
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Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest
Mathematics, 2021 This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transaction
Tzu-Hsuan Lin, Jehn-Ruey Jiang
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An individualization approach for head-related transfer function in arbitrary directions based on deep learning [PDF]
JASA Express Letters, 2022 This paper provides an individualization approach for head-related transfer function (HRTF) in arbitrary directions based on deep learning by utilizing dual-autoencoder architecture to establish the relationship between HRTF magnitude spectrum and ...
Dingding Yao +6 more
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Idea of AE separation from unpredicted source area during AE testing by autoencoder [PDF]
Proceedings of 1st International Electronic Conference on Applied Sciences, 2020 When conducting AE testing, there is an industrial need to separate AE from monitoring area to that from outside of the area in some cases. In this study, usefulness of autoencoder to solve this problem is discussed by simple experiment using an isotropic thin steel ruler.
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Linear local tangent space alignment with autoencoder
Complex & Intelligent Systems, 2023 Linear local tangent space alignment (LLTSA) is a classical dimensionality reduction method based on manifold. However, LLTSA and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space.
Ruisheng Ran, Jinping Wang, Bin Fang
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Multilayer Fisher extreme learning machine for classification
Complex & Intelligent Systems, 2022 As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years.
Jie Lai +4 more
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Applied Sciences, 2019 Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system.
Chu Zhang +4 more
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Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
Applied Sciences, 2022 With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks.
Zhangming Xiong +4 more
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An online deep extreme learning machine based on forgetting mechanism
Dianzi Jishu Yingyong, 2018 The development of deep learning promotes the development of deep online learning, and online learning tends to have strong effectiveness. Based on the principle of online extreme learning machine and the principle of autoencoder of deep extreme learning
Liu Buzhong
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