Results 41 to 50 of about 83,749 (307)
CoRR, 2017 In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.
Alireza Makhzani, Brendan J. Frey
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International Journal of Information Management, 2020 Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management.
H. D. Nguyen +3 more
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Communications Chemistry, 2023 The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a
Toshiki Ochiai +11 more
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Enhancing Radar Resolution and Target Detection Probability with a Denoising Autoencoder [PDF]
Journal of Electromagnetic Engineering and ScienceWe propose the use of a denoising autoencoder to improve radar resolution and target detection probability in noise-contaminated range-Doppler diagrams. Conventionally, target detection has been performed using constant false alarm rate (CFAR) algorithms,
Wonhyo Kim, Daegun Oh, Youngwook Kim
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A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization
SN Applied Sciences, 2021 Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks.
R. Sekhar +3 more
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CoRR, 2023 Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces.
Aymene Mohammed Bouayed, David Naccache
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MVAE: Multimodal Variational Autoencoder for Fake News Detection
The Web Conference, 2019 In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound ...
Dhruv Khattar +3 more
semanticscholar +1 more source
Structuring Autoencoders [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data which are additionally enriched with a desired structure in this low dimensional space. While traditional Autoencoders have proven to structure data naturally they fail to discover semantic structure that is hard to ...
Marco Rudolph +2 more
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Feedback Recurrent Autoencoder [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be ...
Yang Yang 0010 +3 more
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