The authors built a generative model for neural network weights based on flow matching.
Permutation-equivariant flow matching generates neural network weights without alignment
A Graph Meta Network models the distribution of independently trained networks, matching their accuracy, functional similarity, and weight similarity without requiring neuron matching.
Big Tech
Arkadi Piven · Yam Eitan · Guy Bar-Shalom · Fabrizio Frasca · Daniel Cremers · Thomas Dagès · +2 more
Technion – Israel Institute of Technology · Technical University of Munich · Munich Center for Machine Learning · NVIDIA
Research Digest··3 min read
The authors introduce a flow-matching generative model whose velocity field is parameterized by a permutation-equivariant Graph Meta Network.
Why this paper
From NVIDIA and 3 others
In one line
A permutation-equivariant flow matching model generates neural network weights directly from independently trained networks without alignment.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ✓Limitations stated by the authors (3 noted)
- ·No benchmark numbers found
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