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Official implementation of the ARROW-Diff graph generation method.
This is the official implementation of the paper “Griffin: Towards a Graph-Centric Relational Database Foundation Model.”
Implementation of our unlearning method "Partial Model Collapse" introduced in the paper: "Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs" (Preprint).
Code accompanying the paper "Generalized Interpolating Discrete Diffusion"
EDGE: Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
Official Jax Implementation of MD4 Masked Diffusion Models
Reference implementation of the paper "Efficient and Scalable Graph Generation through Iterative Local Expansion"
Metrics to evaluate quality and efficacy of synthetic datasets.
Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.
Machine Learning and Computer Vision Engineer - Technical Interview Questions
⚡ TabPFN: Foundation Model for Tabular Data ⚡
Official Implementations of "Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space""
A package for benchmarking synthetic relational data generation methods
[TMLR] GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
RelBench: Relational Deep Learning Benchmark
TorchCFM: a Conditional Flow Matching library
A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
Processed / Cleaned Data for Paper Copilot
Style guides for Google-originated open-source projects
[ICML 2024 Best Paper] Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution (https://arxiv.org/abs/2310.16834)
Generic template to bootstrap your PyTorch project.
[ICLR 2025] TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
Official Implementation of the Paper "MAGNet: Motif-Agnostic Generation of Molecules from Shapes"