One Algorithm, Two Models, and a Prediction
-
Updated
May 31, 2018 - Python
One Algorithm, Two Models, and a Prediction
Generative Predictive Networks — an experimental attempt to stabilize GANs' training.
Full-stack predictive analytics platform for forecasting production delays and generating real-time operational alerts.
Simple prolog application that uses predicate logic to diagnose diseases.
PredRNN implementation using Tensorflow.
Demo implementations of JEPA World Models to support research
Demand forecasting and inventory optimization using Python, Jupyter, machine learning, and PuLP.
Graph SuperResolution Network using geometric deep learning.
Predicting multigraph brain population from a single graph
A few-shot learning approach to forecasting the evolution of the brain connectome.
Our group project for Govhack2023
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs? (Python3 version)
ABMT (Adversarial Brain Multiplex Translator) for brain graph translation using geometric generative adversarial network (gGAN).
Analysis code for the OpenScope Credit Assignment project.
Federated time-dependent graph evolution prediction with missing timepoints.
Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)
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.
MultiGraphGAN for predicting multiple target graphs from a source graph using geometric deep learning.
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.
To associate your repository with the predictive-learning topic, visit your repo's landing page and select "manage topics."