There are 0 repository under latent-variables topic.
🎓 Tidy tools for academics
Data Analysis on Mental Health.
This is the source code for HDNO: a hierarchical model for task-oriented dialogue system.
AI that generates human faces which have never been seen before. The future is now 😁
PyTorch implementation of "Variational Autoencoders with Jointly Optimized Latent Dependency Structure" [ICLR 2019]
Ωnyx - Structural Equation Modeling
Match Predictions for Professional League of Legends Matches
High-Performance Implementation of Spectral Learning of Latent-Variable PCFGs (Cohen et al., 2013)
Jumping across biomedical contexts using compressive data fusion
Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (ACML 2017)
R package for penalized factor analysis via trust-region algorithm and automatic multiple tuning parameter selection
This R tutorial automates the 3-step ML auxiliary variable procedure using the MplusAutomation package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters. To learn more about auxiliary variable integration methods and why multi-step methods are necessary for producing un-biased estimates see Asparouhov & Muthén (2014).
Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.
A simple Jupyter notebook to visualize data in latent space using dimensionality reduction techniques.
Distributed Non Negative RESCAL decomposition with estimation of latent features
Evaluating preprocessing methods to predict ethnic distributions using names
Implementation of transfer learning approaches for predictive modeling of anticancer drug sensitivity.
This `R` tutorial automates the BCH two-step axiliary variable procedure (Bolk, Croon, Hagenaars, 2004) using the `MplusAutomation` package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters.
GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
This walkthrough is presented by the IMMERSE team and will go through some common tasks carried out in R.
Demonstrate the speed of running an LCA analysis using MplusAutomation
Random Forest of Tensors (RFoT) is a tensor decomposition based ensemble semi-supervised classifier.
Deep Neural Net For Finding Similar Images With Hyperparameter Optimization + AWS And Azure GPU Capabilities
Code used in blog post about dimensionality reduction using Python
Limited Information Goodness of Fit Tests for Binary Factor Models
Evaluation of HMCA for Latent Biomarker Discovery