EDAPINENUT / GNTPP
This is the official codebase of `Exploring Generative Neural Temporal Point Process' (Accepted by TMLR).
☆19Updated last year
Alternatives and similar repositories for GNTPP
Users that are interested in GNTPP are comparing it to the libraries listed below
Sorting:
- Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)☆58Updated 4 months ago
- ☆30Updated 3 years ago
- Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)☆84Updated 4 years ago
- Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)☆23Updated last year
- ☆33Updated 2 years ago
- Paper lists for Temporal Point Process☆111Updated 7 months ago
- ☆59Updated 4 years ago
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆27Updated 2 years ago
- ☆22Updated 4 years ago
- ☆50Updated 2 years ago
- The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021☆35Updated 3 years ago
- This repository contains recent background materials, current works, and codes for researching in TPP.☆16Updated last year
- Variational Autoencoders for Marked Point Processes☆16Updated 4 years ago
- A list of papers for group meeting☆16Updated 4 months ago
- Code for Transformed Distribution Matching (TDM) for Missing Value Imputation, ICML 2023☆14Updated last year
- ☆36Updated 3 years ago
- ☆16Updated 2 years ago
- learning point processes by means of optimal transport and wasserstein distance☆54Updated 7 years ago
- (Pytorch ver) Code for "Fully Neural Network based Model for General Temporal Point Process"☆19Updated 4 years ago
- Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization (NeurIPS 21')☆23Updated 3 years ago
- Attentive Neural Point Processes for Event Forecasting, AAAI 2021☆17Updated 3 years ago
- LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.☆35Updated 2 years ago
- Source code for NeurIPS 2019 paper "Learning Latent Processes from High-Dimensional Event Sequences via Efficient Sampling""☆10Updated 4 years ago
- New structural distributional shifts for evaluating graph models☆17Updated last year
- ☆44Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- ☆12Updated last year
- ☆14Updated 3 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆26Updated 3 years ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago