• GNAR

  • Referenced in 3 articles [sw31350]
  • fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network...
  • DeepAR

  • Referenced in 5 articles [sw38790]
  • DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. Probabilistic forecasting, i.e. estimating the probability distribution...
  • WaveNet

  • Referenced in 20 articles [sw38795]
  • neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with...
  • MADE

  • Referenced in 12 articles [sw36209]
  • introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method ... masks the autoencoder’s parameters to respect autoregressive constraints: each input is reconstructed only from ... probability. We can also train a single network that can decompose the joint probability ... significantly faster and scales better than other autoregressive estimators...
  • autovarCore

  • Referenced in 1 article [sw15182]
  • autovarCore: Automated Vector Autoregression Models and Networks. Automatically find the best vector autoregression models...
  • NADE

  • Referenced in 5 articles [sw36210]
  • present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied ... ordering of input dimensions used by the autoregressive product rule decomposition. Finally, we also show...
  • OKVAR-Boost

  • Referenced in 3 articles [sw24063]
  • networks. MOTIVATION: Reverse engineering of gene regulatory networks remains a central challenge in computational systems ... this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that ... model parameters, as well as the network structure. RESULTS: A flexible boosting algorithm (OKVAR-Boost ... perform the tasks of parameter learning and network inference for the proposed model. Specifically...
  • mlVAR

  • Referenced in 2 articles [sw33247]
  • multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal...
  • RLDDE

  • Referenced in 3 articles [sw35866]
  • based dimension and delay estimator for neural networks in time series prediction. Time series prediction ... traditionally handled by linear models such as autoregressive and moving-average. However they are unable ... linearity in the data. Neural networks are non-linear models that are suitable to handle...
  • SOFAR

  • Referenced in 7 articles [sw31665]
  • constrained optimization to learn the underlying association networks, with broad applications to both unsupervised ... analysis, sparse factor analysis, and spare vector autoregression analysis. Exploiting the framework of convexity-assisted...
  • BSMART

  • Referenced in 6 articles [sw07381]
  • backbone of the BSMART project is Multivariate AutoRegressive (MAR) analysis that has been long developed ... resolution, functional relations within large scale brain networks...
  • SSNbayes

  • Referenced in 1 article [sw41235]
  • Bayesian spatio-temporal models for stream networks” . In these models, spatial dependence is captured using ... while temporal autocorrelation is modelled using vector autoregression methods...
  • Flow++

  • Referenced in 2 articles [sw35017]
  • performance compared to state-of-the-art autoregressive models. In this paper, we investigate ... purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow ... state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks...
  • FloWaveNet

  • Referenced in 2 articles [sw35018]
  • time audio synthesis capability by incorporating inverse autoregressive flow for parallel sampling. However, these approaches ... training pipeline with a well-trained teacher network and can only produce natural sound...
  • SparseTSCGM

  • Referenced in 1 article [sw15374]
  • time series chain graphical models. Computes sparse autoregressive coefficient and precision matrices for time series ... undirected graphs for instantaneous interactions) and Bayesian networks (directed graphs for dynamic interactions) for reconstructing...
  • psychonetrics

  • Referenced in 3 articles [sw34796]
  • Gaussian graphical models (GGMs) -- an undirected network model of partial correlations -- between observed variables ... takes the form of a graphical vector-autoregression model between latent variables and is termed...
  • MaskGAN

  • Referenced in 3 articles [sw31828]
  • Neural text generation models are often autoregressive language models or seq2seq models. These models generate ... improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce...
  • GraphRNN

  • Referenced in 1 article [sw36060]
  • generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling ... graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges...
  • ARfit

  • Referenced in 38 articles [sw00046]
  • ARfit is a collection of Matlab modules for...
  • Expokit

  • Referenced in 200 articles [sw00258]
  • Expokit provides a set of routines aimed at...