DeepMind & Stanford U’s UNFs: Advancing Weight-Space Modeling with Universal Neural Functionals | Synced
A research team from Google DeepMind and Stanford University introduces a groundbreaking algorithm known as universal neural functionals (UNFs), which autonomously constructs permutation-equivarian...
Source: Synced | AI Technology & Industry Review
A research team from Google DeepMind and Stanford University introduces a groundbreaking algorithm known as universal neural functionals (UNFs), which autonomously constructs permutation-equivariant models for any weight space, offering a versatile solution to the architectural constraints encountered in prior works.