Jeongwhan Choi's repositories
yonsei-poster
LaTeX beamer poster template themed for the Yonsei University
SGMC-AAAI21
Code for AAAI21 paper "Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning"
BARS
BARS: Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
bottleneck
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
DALL-E
PyTorch package for the discrete VAE used for DALL·E.
denoising-diffusion-gan
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs https://arxiv.org/abs/2112.07804
diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
einops
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)
finite-element-networks
Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022
graph-neural-pde
Continuous Diffusion Graph Neural Network
H2GCN
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
JacobiConv
How Powerful are Spectral Graph Neural Networks
LATTICE
[ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"
lessr
Handling Information Loss of Graph Neural Networks for Session-based Recommendation
MP-Neural-PDE-Solvers
Repo to the paper "Message Passing Neural PDE Solvers"
neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms for time series data :wavy_dash:.
tracebase
Multivariate time series forecasting on high-dimensional and sparse Uber movement speed data.
ViT-Anti-Oversmoothing
[ICLR 2022] "Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice" by Peihao Wang, Wenqing Zheng, Tianlong Chen, Zhangyang Wang