Overview
| Date | One-day workshop on Sunday April 27th, 2025 |
| Location | The workshop will be held in person in Singapore. Remote access will be made available at a later date. |
| Submission | |
| Accepted papers | Thanks to our organizing committee, we are can now share the list of accepted papers |
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 the weight space,
symmetries, scaling laws, and model zoo datasets. - Model analysis: Inferring model properties, such as test performance
or generalization error, by the inspection of their weights. - Model synthesis: Generating model weights for specific datasets and tasks,
and improving tasks like pruning, merging, and robustness through weight manipulation. - Learning from populations: Using data from neural network populations to identify
and exploit structure in trained models. - Applications to neural fields and beyond: Applying weight space learning to 3D shape analysis, neural radiance fields (NeRFs), and other domains.
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?