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Once the network is trained using the conditional variational autoencoder loss, ... [Google Scholar]; Stuart, A.M. Inverse problems: A Bayesian perspective.

23.09.2019 · Face images generated with a Variational Autoencoder (source: Wojciech Mormul on Github). In a pr e vious post, published in January of this year, we discussed in depth Generative Adversarial Networks (GANs) and showed, in particular, how adversarial training can oppose two networks, a generator and a discriminator, to push both of them to improve iteration after iteration.

01.09.2021 · Variational AutoEncoder (VAE) as a class of neural networks performing nonlinear dimensionality reduction has become an effective tool in neuroimaging analysis. Currently, most studies on VAE consider unsupervised learning to capture the latent representations and to some extent, this strategy may be under-explored in the case of heavy noise and imbalanced neural …

Apr 01, 2021 · In this paper, we proposed a neural network (variational autoencoder) architecture that is used to generate an ECG corresponding to a single cardiac cycle. Our method generates synthetic ECGs using rather small number (25) of features, with completely natural appearance, which can be used to augment the training sets in supervised learning ...

Cited by. Year. OC-FakeDect: Classifying deepfakes using one-class variational autoencoder. H Khalid, SS Woo. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …. , 2020. 28. 2020. Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors.

16.12.2021 · Pu Y, Gan Z, Henao R, Yuan X, Li C, Stevens A, Carin L (2016) Variational autoencoder for deep learning of images, labels and captions. In: Advances in neural information processing systems, pp 2352–2360 Google Scholar

17.02.2021 · Abstract. In this chapter, we introduce generative models. We focus specifically on the Variational Autoencoder (VAE) family, which uses the same set of tools introduced in Chap. 3, but with a stark objective in mind.Here, we are interested in modeling the process that generates the …

Keywords: deep learning, variational autoencoders, data representation learning, generative models, ... 20202012.13392 [Google Scholar].

The method proposed in this work addresses these issues using a two-prong strategy: (1) we use a robust variational autoencoder model that is based on robust statistics, specifically the β-divergence that can be trained with data that has outliers; (2) we use a transfer-learning method for learning models across datasets with different ...

Feb 17, 2021 · Rosca M, Lakshminarayanan B, Warde-Farley D, Mohamed S (2017) Variational approaches for auto-encoding generative adversarial networks. arXiv e-prints 1706.04987 Google Scholar 48. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X, Chen X (2016) Improved techniques for training GANs.

I'm a machine learning researcher, since 2018 at Google. My contributions include the Variational Autoencoder (VAE), the Adam optimizer, ...

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Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen: Variational Autoencoders and Nonlinear ICA: A Unifying Framework.

A variational autoencoder (VAE) is a type of unsupervised neural network which is able ... Go to reference in articleCrossrefGoogle Scholar.

Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.

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2019;234(6):1–8. Google Scholar. 4. Asperti A. About generative aspects of variational autoencoders. In: Machine Learning, Optimization, and ...

I'm a Research Scientist at Google. Some of my research contributions are the Variational Auto-Encoder (VAE), a framework for semi-supervised and ...

19.08.2021 · To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. Specifically, the principal component analysis technique is adopted to realize feature reduction, ... Google Scholar; 2. A. Nakayama, ...

Aug 25, 2020 · Using a variational autoencoder, we are able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that show dose-responsive antimicrobial activity.

In this work, we provide an introduction to variational autoencoders and some important ... NASA ADS · Google Scholar · Semantic Scholar ...

Research Scientist, Google Brain - ‪‪Cited by 127001‬‬ - ‪Machine Learning‬ - ‪Deep Learning‬ - ‪Neural Networks‬ - ‪Generative Models‬ - ‪Variational‬ ...

Variational Autoencoders (VAEs) The VAE implemented here uses the setup found in most VAE papers: a multivariate Normal distribution for the conditional distribution of the latent vectors given and input image (q ϕ (z | x i) in the slides) and a multivariate Bernoulli distribution for the conditional distribution of images given the latent vector (p θ (x | z) in the slides).

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22.10.2019 · Recently, it has been shown that variational autoencoders (VAEs) can be successfully trained to learn such codes in unsupervised and semi-supervised scenarios. In this paper, we show that a variational autoencoder with binary latent variables leads to a more natural and effective hashing algorithm that its continuous counterpart.

Aug 10, 2021 · Understanding Variational Autoencoder. Let us consider some dataset X = {x (i)} N i=1 consisting of N i.i.d. samples of some continuous or discrete variable x. We assume that the data are generated by some random process, involving an unobserved continuous random variable z. The process consists of two steps: (1) a value z (i) is generated from ...