Results 21 to 30 of about 247,960 (211)
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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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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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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, 2021 This repo includes the datasets that are used to benchmark the experiment results of paper titled "CODE-AE: A Coherent De-confounding Autoencoder for Predicting Patient-Specific Drug Response From Cell Line ...
Di He
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Classes in ftgo-microservice and AE-C decomposition
, 2023 This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with C-means (AE-C)
Anonymous
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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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OF-AE: Oblique Forest AutoEncoders
, 2023 In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1].
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A Hybrid Autoencoder Network for Unsupervised Image Clustering
Algorithms, 2019 Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision.
Pei-Yin Chen, Jih-Jeng Huang
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, 2023 Timely identification of outliers occurring in key performance indicators (KPIs) of mobile cellular networks is crucial for prompt action to unexpected events.
Benjamin Premkumar Annamalai (10759098) +6 more
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