Discover rpatrik96/nl-causal-representations Open Source project

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rpatrik96

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This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).

pytorch-a2c

A well-documented A2C written in PyTorch

Python

AttA2C

Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning

Jupyter Notebook

3d-tracking

Object tracking with OpenCV based on stereo camera images

Python

ima-vae

This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivalent to optimizing the IMA-regularized log-likelihood under certain assumptions (e.g., small decoder variance).

Jupyter Notebook