SDS-Lab / QW_LossLinks
A Quasi-Wasserstein Loss for Learning Graph Neural Networks (QW loss)
☆11Updated last year
Alternatives and similar repositories for QW_Loss
Users that are interested in QW_Loss are comparing it to the libraries listed below
Sorting:
- Learnable Global Pooling Layers Based on Regularized Optimal Transport (ROT)☆16Updated last year
- EDGE: Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling☆66Updated 3 months ago
- Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022)…☆61Updated 2 years ago
- [ICML2025] The official implementation of "PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models"☆31Updated 3 months ago
- Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation☆34Updated 2 years ago
- [ICML 2024] Code for Pairwise Alignment Improves Graph Domain Adaptation (Pair-Align)☆14Updated last year
- Official Code Repository for the paper "Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations" (I…☆173Updated last year
- ☆29Updated last year
- [NeurIPS'23] Source code of "Data-Centric Learning from Unlabeled Graphs with Diffusion Model": A data-centric transfer learning framewor…☆21Updated 3 months ago
- The code for GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning☆62Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆44Updated 2 years ago
- [ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.☆24Updated 2 years ago
- Official implementation for Learning Invariant Molecular Representation in Latent Discrete Space (NeurIPS 2023)☆21Updated last year
- Official Code Repository for the paper "Graph Generation with Diffusion Mixture" (ICML 2024).☆34Updated last year
- Code for GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts☆16Updated 8 months ago
- Source code for the paper 'Uncovering Neural Scaling Laws in Molecular Representation Learning' (NeurIPS 2023 Datasets and Benchmarks).☆14Updated last year
- Official Implementation of "D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion"☆23Updated last year
- [NeurIPS 2023] "Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules"☆39Updated last year
- [ICLR 2024] VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs☆102Updated last year
- [CIKM2023] The official implementation of "MPerformer: An SE(3) Transformer-based Molecular Perceptron"☆28Updated 10 months ago
- ☆24Updated 2 years ago
- Edge-Augmented Graph Transformer☆78Updated last year
- ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks(ICLR 2023)☆20Updated last year
- [AAAI2023 Oral] The official implementation of "Hierarchical Contrastive Learning for Temporal Point Processes"☆26Updated 5 months ago
- A list of papers for group meeting☆17Updated 3 weeks ago
- [NeurIPS 2024] Implementation of "Enhancing Graph Transformers with Hierarchical Distance Structural Encoding"☆15Updated 4 months ago
- [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?☆22Updated last year
- ☆16Updated 2 years ago
- The official source code for "Conditional Graph Information Bottleneck for Molecular Relational Learning".☆43Updated 2 years ago
- Official Pytorch implementation of NeuralWalker (ICLR 2025)☆35Updated 2 months ago