PaskardLi's repositories
vasile-paskardlgm.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Time-Series-Library
A Library for Advanced Deep Time Series Models.
Coarsening-on-Graphs-for-Scalable-GNNs
Project about proceeding graph coarsening techniques for scaling up GNN models.
pyamg
Algebraic Multigrid Solvers in Python
LibMTL
A PyTorch Library for Multi-Task Learning
pGNNs
p-Laplacian Based Graph Neural Networks (ICML'2022)
SEAL
SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). "M. Zhang, Y. Chen, Link Prediction Based on Graph Neural Networks, NeurIPS 2018 spotlight".
PEG
[ICLR-2022]: Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
ESAN
Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
BernNet
PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"
GSN
Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxiv.org/abs/2006.09252
Matern-Gaussian-Processes-on-Graphs
Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".
distance-encoding
Distance Encoding for GNN Design
Stochastic-Deep-Gaussian-Processes-over-Graphs
Codes for paper "Stochastic Deep Gaussian Processes over Graphs"
mixhop
Official Implementation of ICML 2019 Paper. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing; and UAI 2019 Paper: N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Deep-Gaussian-Processes-with-Doubly-Stochastic-Variational-Inference
Deep Gaussian Processes with Doubly Stochastic Variational Inference
GGP
Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'