Results 11 to 20 of about 13,735 (297)
AD-VAE: Adversarial Disentangling Variational Autoencoder [PDF]
SensorsFace recognition (FR) is a less intrusive biometrics technology with various applications, such as security, surveillance, and access control systems. FR remains challenging, especially when there is only a single image per person as a gallery dataset ...
Adson Silva, Ricardo Farias
doaj +2 more sources
Sensors, 2017 The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase
Manuel Lopez-Martin +3 more
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Grammar Variational Autoencoder [PDF]
, 2017 Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete data such as arithmetic expressions and molecular structures still poses significant challenges. Crucially, state-of-the-art methods often produce outputs that are not valid.
Matt J. Kusner +2 more
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Variational autoencoder architecture. [PDF]
, 2021 Variational autoencoder architecture.
Rafael Geraldeli Rossi (11873826) +2 more
core +1 more source
altosaar/variational-autoencoder: Canonical release [PDF]
, 2021 Release to address requests for ...
Ilya V. Schurov +4 more
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Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Manifold-valued data naturally arises in medical imaging. In cognitive neuroscience, for instance, brain connectomes base the analysis of coactivation patterns between different brain regions on the analysis of the correlations of their functional Magnetic Resonance Imaging (fMRI) time series - an object thus constrained by construction to belong to ...
Nina Miolane, Susan P. Holmes
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Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimisation [PDF]
, 2022 In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model.
Marian, Dryzhakov +3 more
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Schematics for autoencoder and variational autoencoder. [PDF]
, 2023 Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An autoencoder is a deterministic model where z is a mapping of the input data. B) A variational autoencoder is a probabilistic model where the mapping z is
Mostafa Eltager (17102176) +5 more
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A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes [PDF]
, 2023 Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and optimized properties.
Tzuhsiung, Yang +3 more
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Variational Autoencoders Without the Variation
CoRR, 2022 11 pages, 7 figures, 3 ...
Gregory A. Daly +2 more
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