crazysal / VariationalRNNLinks
A Recurrent Latent Variable Model for Sequential Data
☆28Updated 7 years ago
Alternatives and similar repositories for VariationalRNN
Users that are interested in VariationalRNN are comparing it to the libraries listed below
Sorting:
- ☆92Updated 2 years ago
- code for "Fully Neural Network based Model for General Temporal Point Processes"☆64Updated 4 years ago
- learning point processes by means of optimal transport and wasserstein distance☆54Updated 7 years ago
- Pytorch implementation of the Variational Recurrent Neural Network (VRNN).☆291Updated 4 years ago
- Modeling the asynchronous event sequence via Recurrent Point Process☆61Updated 7 years ago
- State space modeling with recurrent neural networks☆45Updated 7 years ago
- Source code of the neural Hawkes particle smoothing (ICML 2019)☆44Updated 6 years ago
- Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)☆89Updated 4 years ago
- ☆40Updated 7 years ago
- Deep Markov Models☆132Updated 6 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆100Updated 6 years ago
- An encoder-decoder framework for learning from incomplete data☆45Updated 2 years ago
- A short course on temporal point process and modeling irregular time series☆21Updated 5 years ago
- Recurrent Marked Temporal Point Processes☆56Updated 4 years ago
- Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)☆38Updated 8 years ago
- Kalman Variational Auto-Encoder☆138Updated 6 years ago
- Gated Recurrent Unit with a Decay mechanism for Multivariate Time Series with Missing Values☆121Updated 6 years ago
- Variational Auto-Encoders in a Sequential Setting.☆177Updated 7 years ago
- Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)☆62Updated last year
- A PyTorch implementation of a Deep Hidden Markov Model [Structured Inference Networks for Nonlinear State Space Models]☆58Updated last year
- Implementation of the MIWAE method for deep generative modelling of incomplete data sets.☆41Updated last year
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'☆38Updated 7 years ago
- Unsupervised clustering with (Gaussian mixture) VAEs☆301Updated 9 years ago
- Code and data for "Deep Reinforcement Learning of Marked Temporal Point Processes", NeurIPS 2018☆81Updated 6 years ago
- A Point Process Toolbox Based on PyTorch☆138Updated 5 years ago
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆47Updated 5 years ago
- Experimental Codes for Temporal Regularized Matrix Factoriztion for High-dimensional Time Series Prediction.☆79Updated 6 years ago
- Bayesian Neural Network in PyTorch☆93Updated last year
- PPG (Point Process Generator) is a Reinforcement Learning framework that is able to produce actions by imitating expert sequences.☆14Updated 6 years ago