S-Data Science Lab

S-Data Science Lab's repositories

A list of papers for group meeting

MIT600

Householder Reflection Adaptation

Language:Python200

000

A Quasi-Wasserstein Loss for Learning Graph Neural Networks (QW loss)

Language:Python700

000

[CIKM2023] The official implementation of "MPerformer: An SE(3) Transformer-based Molecular Perceptron"

Language:PythonGPL-3.0400

Learnable Global Pooling Layers Based on Regularized Optimal Transport (ROT)

Language:PythonMIT1500

A list of awesome papers and cool resources on hypercomplex neural networks.

500

Language:Python900

AAA24_Hierarchical Contrastive Learning for Temporal Point Processes

200

"Weakly-Supervised Temporal Action Alignment Driven by Unbalanced Spectral Fused Gromov-Wasserstein Distance"

000

A Gromov-Wasserstein Learning Framework for Graph Partitioning, Matching, and Node Embedding (ICML-19, NeurIPS-19)

Language:Python100

Gromov-Wasserstein Factorization Model (AAAI-20, TPAMI-22)

Language:PythonGPL-3.0000