• GTM

  • Referenced in 57 articles [sw39434]
  • Mapping. Latent variable models represent the probability density of data in a space of several ... terms of a smaller number of latent, or hidden, variables. A familiar example is factor ... space. In this paper we introduce a form of non-linear latent variable model called...
  • DeepWalk

  • Referenced in 63 articles [sw39604]
  • space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling ... obtained from truncated random walks to learn latent representations by treating walks as the equivalent...
  • latentnet

  • Referenced in 21 articles [sw10550]
  • approach to modeling networks based on positing the existence of an latent space of characteristics ... space. It also includes a variant of the extension of the latent position model...
  • HLSM

  • Referenced in 1 article [sw36719]
  • package HLSM: Hierarchical Latent Space Network Model. Implements Hierarchical Latent Space Network Model (HLSM...
  • NICE

  • Referenced in 15 articles [sw29631]
  • distribution that is easy to model. For this purpose, a non-linear deterministic transformation ... learned that maps it to a latent space so as to make the transformed data ... factorized distribution, i.e., resulting in independent latent variables. We parametrize this transformation so that computing ... show that this approach yields good generative models on four image datasets...
  • VideoFlow

  • Referenced in 1 article [sw35019]
  • describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative...
  • dynr

  • Referenced in 3 articles [sw18446]
  • kinds in R. These include models of processes in discrete time or continuous time. They ... continuous (e.g. state space models) or discrete (e.g. regime-switching models). The general approach involves ... estimation of single- and multi-subject models of latent time series with the extended Kalman...
  • GANomaly

  • Referenced in 5 articles [sw41240]
  • introduce such a novel anomaly detection model, by using a conditional generative adversarial network that ... latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model ... network maps this generated image to its latent representation. Minimizing the distance between these images...
  • spate

  • Referenced in 6 articles [sw12265]
  • space, a linear Gaussian state space model is obtained. When doing inference, the main computational ... spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach ... aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal...
  • collpcm

  • Referenced in 1 article [sw36718]
  • latent position cluster models or social networks, which includes searches over the model space (number...
  • abc-sde

  • Referenced in 3 articles [sw24744]
  • performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations ... SDEs) and not limited to the ”state-spacemodelling framework. Both one- and multi-dimensional...
  • subgraph2vec

  • Referenced in 4 articles [sw36496]
  • continuous vector space, which is easily exploited by statistical models for tasks such as graph ... from neighbourhoods of nodes to learn their latent representations in an unsupervised fashion. We demonstrate...
  • LSAfun

  • Referenced in 1 article [sw30382]
  • convenient working with latent semantic analysis (LSA) and other vector space models of semantics...
  • BoxE

  • Referenced in 1 article [sw41383]
  • BoxE: A Box Embedding Model for Knowledge Base Completion. Knowledge base completion (KBC) aims ... into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject...
  • LVGP

  • Referenced in 1 article [sw33790]
  • modeling, predict responses for new inputs, and plot latent variables locations in the latent space ... another GP modeling package ”GPM”. The modeling method is published in ”A Latent Variable Approach...
  • TransG

  • Referenced in 4 articles [sw34443]
  • symbolic entities and relations into continuous vector space, has become a new, hot topic ... Gaussian mixture model for embedding, TransG. The new model can discover latent semantics...
  • TTLocVis

  • Referenced in 2 articles [sw40767]
  • generate topic distributions from Latent Dirichlet Allocation (LDA) Topic Models for geo-coded Tweets ... public discourse on various topics in space and time. The package can be used...
  • semds

  • Referenced in 1 article [sw42291]
  • Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities ... dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within ... objects are represented in a low-dimensional space...
  • InfoGraph

  • Referenced in 2 articles [sw37754]
  • also some recent methods based on language models (e.g. graph2vec) but they tend to only ... from unlabeled data while preserving the latent semantic space favored by the current supervised task ... with state-of-the-art semi-supervised models...
  • timedelay

  • Referenced in 0 articles [sw17892]
  • profile likelihood approaches. The model is based on a state-space representation for irregularly observed ... time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method ... Gibbs sampler, producing posterior samples of the model parameters that include the time delay...