# Workshop on Neural Network Weights as a New Data Modality

## Overview
The recent surge in the number of publicly available neural network models—exceeding a million on platforms like Hugging Face—calls for a shift in how we perceive neural network weights. This workshop aims to establish neural network weights as a new data modality, offering immense potential across various fields.

We plan to address key dimensions of weight space learning:

- Weight Space as a Modality
    - Characterization of weight space properties such as symmetries (e.g., permutations, scaling, and beyond).
    - Weight space augmentations, scaling laws, model zoo datasets, etc.
- Weight Space Learning Tasks/Learning Paradigms
    - Supervised approaches: Weight embeddings, meta-learning networks, (graph) hyper-networks.
    - Unsupervised approaches: Autoencoders or hyper-representations.
    - Weight space learning backbones: Plain MLPs, transformers, equivariant architectures (e.g., GNNs and neural functionals).
- Theoretical Foundations
    - Expressivity of weight space processing modules.
    - Theoretical analysis of model weight properties.
    - Generalization bounds of weight space learning methods.
- Model/Weight Analysis
    - Inferring model properties and behaviors from their weights.
    - Investigating neural lineage and model trees through weights.
    - Learning dynamics in population-based training.
    - Interpretability of models via their weights.
- Model/Weight Synthesis and Generation
    - Modeling weight distributions to facilitate weight sampling.
    - Generating weights in the context of transfer learning, learnable optimizers, implicit neural representation (INR) synthesis.
    - Model operations/editing (e.g., model merging, model soups, model pruning, task arithmetic).
    - Meta-learning and continual learning using model weights.
- Applications of Weight Space Learning
    - Computer vision tasks: Using NeRFs/INRs.
    - Applications to physics and dynamical system modeling.
    - Backdoor detection and adversarial robustness in weight space.

Weight space learning remains a nascent and scattered research area. Our goal is to provide a bridge between the abovementioned topics, and research areas such as model merging, neural architecture search, and meta-learning. By aligning terminology and methodologies, we aim to drive sustained progress and foster interdisciplinary collaboration.

## Research Goals and Key Questions
This workshop will explore fundamental questions about weight spaces, such as:

- What properties of weights, such as symmetries and invariances, present challenges or can be leveraged for optimization, learning and generalization?
- How can model weights be efficiently represented, manipulated, and used for downstream tasks?
- What model information can be decoded from model weights?
- Can model weights be generated for specific applications, to make training and model selection more efficient?
- Can weight space learning benefit research in processing and synthesising neural fields, for e.g. scientific applications and 3D vision?
- How can we democratize the usage of weight spaces, enabling more efficient research progress?