OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
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Updated
Sep 4, 2024 - Python
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
Analysis code for the OpenScope Credit Assignment project.
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.
Our group project for Govhack2023
PredRNN implementation using Tensorflow.
MultiGraphGAN for predicting multiple target graphs from a source graph using geometric deep learning.
Federated time-dependent graph evolution prediction with missing timepoints.
Predicting multigraph brain population from a single graph
Simple prolog application that uses predicate logic to diagnose diseases.
A Python toolbox for predicting brain network (graph) evolution over time from a single observation. The codes of the 20 competing Kaggle teams along with the competition datasets are made available.
A few-shot learning approach to forecasting the evolution of the brain connectome.
Graph SuperResolution Network using geometric deep learning.
ABMT (Adversarial Brain Multiplex Translator) for brain graph translation using geometric generative adversarial network (gGAN).
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs? (Python3 version)
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Generative Predictive Networks — an experimental attempt to stabilize GANs' training.
One Algorithm, Two Models, and a Prediction
Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)
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