Results 51 to 60 of about 13,735 (297)
Conditioned Variational Autoencoder for Top-N Item Recommendation [PDF]
, 2022 State-of-the-art recommender systems (RSs) generally try to improve the overall recommendation quality. However, users usually tend to explicitly filter the item set based on available categories, e.g., smartphone brands, movie genres.
Mirko Polato +7 more
core +1 more source
Variational Autoencoder With Optimizing Gaussian Mixture Model Priors
IEEE Access, 2020 The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem.
Chunsheng Guo +5 more
doaj +1 more source
Time Series Anomaly Detection Using Variational Autoencoder [PDF]
, 2021 Rad se bavi primjenom metoda dubokog učenja za otkrivanje anomalije u vremenskim nizovima. Razrađen je teorijski opis modela varijacijskog autoenkodera i odrađeno je njegovo uvježbavanje.
Papić, Jozo
core
Epitomic Variational Graph Autoencoder [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of latent variables fail to capture any information about the input data and the corresponding hidden ...
Rayyan Ahmad Khan +2 more
openaire +2 more sources
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder [PDF]
, 2022 In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT.
Girin, Laurent +5 more
core +1 more source
Ladder Variational Autoencoders
, 2016 Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models.
Sønderby, Casper Kaae +4 more
openaire +4 more sources
Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model [PDF]
, 2021 We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for the entire ...
Tam, Ka-Ming +3 more
core +5 more sources
Symbolic expression generation via variational auto-encoder [PDF]
PeerJ Computer Science, 2023 There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions.
Sergei Popov +4 more
doaj +2 more sources
Anomaly Detection Using Autoencoder With Feature Vector Frequency Map
IEEE Access, 2021 Anomaly detection uses various machine learning techniques to identify and classify defective data on the production line. The autoencoder-based anomaly detection method is an unsupervised method that classifies abnormal samples using an autoencoder ...
Young-Gyu Kim, Tae-Hyoung Park
doaj +1 more source
Variationally Inferred Sampling through a Refined Bound
Entropy, 2021 In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation.
Víctor Gallego, David Ríos Insua
doaj +1 more source