Causing: CAUsal INterpretation using Graphs

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]

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.

High-Performance Implementation of Spectral Learning of Latent-Variable PCFGs (Cohen et al., 2013)

Match Predictions for Professional League of Legends Matches

Jumping across biomedical contexts using compressive data fusion

[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".

Distributed Non Negative RESCAL decomposition with estimation of latent features

Random Forest of Tensors (RFoT) is a tensor decomposition based ensemble semi-supervised classifier.

Code used in blog post about dimensionality reduction using Python

Dataframe dimensionality reduction with a VAE

Clustering with feature extracted from auto encoder gives better result than K-Means.

Pipeline Consisting of LSTM Variational and Transformer Based Autoencoders PCA/UMAP (Parameterized and Non-Parameterized) For Generating Low-Dim Manifold Representation of V1 Neural Activity