https://github.com/ege-erdogan/awesome-weight-space-learning
Awesome papers on weight-space learning
https://github.com/ege-erdogan/awesome-weight-space-learning
List: awesome-weight-space-learning
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Awesome papers on weight-space learning
- Host: GitHub
- URL: https://github.com/ege-erdogan/awesome-weight-space-learning
- Owner: ege-erdogan
- Created: 2024-12-03T07:26:10.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-03-31T06:28:34.000Z (about 2 months ago)
- Last Synced: 2025-04-07T03:01:36.776Z (about 2 months ago)
- Topics: awesome-lists, deep-learning, machine-learning, weight-space-learning
- Homepage:
- Size: 360 KB
- Stars: 12
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-weight-space-learning - Awesome papers on weight-space learning. (Other Lists / Julia Lists)
README
# 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](#awesome-weight-space-learning)
- [Papers](#papers)
- [Architectures / Training](#architectures--training)
- [Weight-Space Generative Models](#weight-space-generative-models)
- [Datasets](#datasets)
- [Other](#other)
- [PhD/MSc Theses](#phdmsc-theses)
- [Blogs, Talks, etc.](#blogs-talks-etc)
---
## Papers
### Architectures / Training
* Meynent et al - [Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction](http://arxiv.org/abs/2503.17138) (2025)
* Putterman et al - [Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models](https://arxiv.org/abs/2410.04207) (2024)
* Schürholt et al - [Towards Scalable and Versatile Weight Space Learning](http://arxiv.org/abs/2406.09997) (2024)
* Tran et al - [Monomial Matrix Group Equivariant Neural Functional Networks](https://arxiv.org/abs/2409.11697) (2024)
* Kalogeropoulos et al - [Scale Equivariant Graph Metanetworks](http://arxiv.org/abs/2406.10685) (2024)
* Kofinas et al - [Graph Neural Networks for Learning Equivariant Representations of Neural Networks](https://doi.org/10.48550/arXiv.2403.12143) (2024)
* Zhou et al - [Universal Neural Functionals](https://doi.org/10.48550/arXiv.2402.05232) (2024)
* Zhou et al - [Permutation Equivariant Neural Functionals](https://doi.org/10.48550/arXiv.2302.14040) (2023)
* Zhou et al - [Neural Functional Transformers](https://papers.nips.cc/paper_files/paper/2023/hash/6e2290d3b6168f9747f38786e40c21f2-Abstract-Conference.html) (2023)
* Lim et al - [Graph Metanetworks for Processing Diverse Neural Architectures](https://doi.org/10.48550/arXiv.2312.04501) (2023)
* Andreis et al - [Set-based Neural Network Encoding Without Weight Tying](https://arxiv.org/abs/2305.16625) (2023)
* Navon et al - [Equivariant Architectures for Learning in Deep Weight Spaces](https://doi.org/10.48550/arXiv.2301.12780) (2023)
* Unterthiner et al - [Predicting Neural Network Accuracy from Weights](https://doi.org/10.48550/arXiv.2002.11448) (2021)
### Weight-Space Generative Models
* Saragih et al - [Flow to Learn: Flow Matching on Neural Network Parameters](http://arxiv.org/abs/2503.19371) (2025)
* Wang et al - [Recurrent Diffusion for Large-Scale Parameter Generation](https://arxiv.org/abs/2501.11587) (2025)
* Soro et al - [Diffusion-Based Neural Network Weights Generation](https://arxiv.org/abs/2402.18153) (2024)
* Wang et al - [Neural Network Diffusion](https://arxiv.org/abs/2402.13144) (2024)
* Erkoç et al - [HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion](https://openaccess.thecvf.com/content/ICCV2023/html/Erkoc_HyperDiffusion_Generating_Implicit_Neural_Fields_with_Weight-Space_Diffusion_ICCV_2023_paper.html) (2023)
* Schürholt et al - [Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights](https://proceedings.neurips.cc/paper_files/paper/2022/hash/b2c4b7d34b3d96b9dc12f7bce424b7ae-Abstract-Conference.html) (2022)
* Peebles et al - [Learning to Learn with Generative Models of Neural Network Checkpoints](https://doi.org/10.48550/arXiv.2209.12892) (2022)
### Datasets
* Schürholt et al - [Model Zoos: A Dataset of Diverse Populations of Neural Network Models](https://proceedings.neurips.cc/paper/2022/hash/f3b7e5d3eb074cde5b76e26bc0fb5776-Abstract-Conference.html) (2022)
### Other
* Plattner et al - [Shape Generation via Weight Space Learning](https://arxiv.org/abs/2503.21830) (2025)
* Kahana et al - [Deep Linear Probe Generators for Weight Space Learning](https://doi.org/10.48550/arXiv.2410.10811) (2024)
* Lim et al - [The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof](https://arxiv.org/abs/2405.20231) (2024)
* Navon et al - [Equivariant Deep Weight Space Alignment](https://doi.org/10.48550/arXiv.2310.13397) (2024)
* Shamsian et al - [Improved Generalization of Weight Space Networks via Augmentations](https://doi.org/10.48550/arXiv.2402.04081) (2024)
* Schürholt et al - [Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction](https://proceedings.neurips.cc/paper_files/paper/2021/hash/89562dccfeb1d0394b9ae7e09544dc70-Abstract.html) (2021)
## PhD/MSc Theses
* Erdogan E. - [Geometric Flow Models over Neural Network Weights](https://github.com/ege-erdogan/msc-thesis) (2025)
* Schürholt K. - [Hyper-Representations: Learning from Populations of Neural Networks](https://arxiv.org/abs/2410.05107) (2024)
## Blogs, Talks, etc.
* [Weight-Space Learning Workshop @ ICLR '25](https://weight-space-learning.github.io/)