README.md

Awesome Weight Space Learning

Weight-space learning is a rapidly-growing area within machine learning, centered on learning functions of neural network weights. This repository is a collection of weight-space learning papers.

It is not exhaustive and will likely never be, but feel free to open a PR if you want to see a paper listed (self-promotion is welcome 🙃).

Table of Contents

• Awesome Weight Space Learning
  • Papers
    • Architectures
    • Weight-Space Generative Models
    • Applications
    • Datasets
    • Other
  • PhD/MSc Theses
  • Blogs, Talks, etc.

---

Papers

Architectures

• Schürholt et al - Towards Scalable and Versatile Weight Space Learning (2024)
• Kalogeropoulos et al - Scale Equivariant Graph Metanetworks (2024)
• Kofinas et al - Graph Neural Networks for Learning Equivariant Representations of Neural Networks (2024)
• Zhou et al - Universal Neural Functionals (2024)
• Zhou et al - Permutation Equivariant Neural Functionals (2023)
• Zhou et al - Neural Functional Transformers (2023)
• Lim et al - Graph Metanetworks for Processing Diverse Neural Architectures (2023)
• Navon et al - Equivariant Architectures for Learning in Deep Weight Spaces (2023)
• Unterthiner et al - Predicting Neural Network Accuracy from Weights (2021)

Weight-Space Generative Models

• Wang et al - Recurrent Diffusion for Large-Scale Parameter Generation (2025)
• Soro et al - Diffusion-Based Neural Network Weights Generation (2024)
• Wang et al - Neural Network Diffusion (2024)
• Erkoç et al - HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion (2023)
• Schürholt et al - Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights (2022)
• Peebles et al - Learning to Learn with Generative Models of Neural Network Checkpoints (2022)

Applications

• Schürholt et al - Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction (2021)

Datasets

• Schürholt et al - Model Zoos: A Dataset of Diverse Populations of Neural Network Models (2022)

Other

• Kahana et al - Deep Linear Probe Generators for Weight Space Learning (2024)
• Navon et al - Equivariant Deep Weight Space Alignment (2024)
• Shamsian et al - Improved Generalization of Weight Space Networks via Augmentations (2024)

PhD/MSc Theses

• Erdogan E. - Geometric Flow Models over Neural Network Weights (2025)
• Schürholt K. - Hyper-Representations: Learning from Populations of Neural Networks (2024)

Blogs, Talks, etc.

• Weight-Space Learning Workshop @ ICLR '25