Imagine if we could reformulate the foundation of deep learning, making it more efficient and interpretable. That’s exactly what a new meta-framework proposes – a symmetry-based approach that could change the way we build and understand deep learning models.
The idea is to redefine the primitive functions in deep learning, moving away from element-wise forms that are currently prevalent. This new approach, dubbed ‘isotropic deep learning’, could have significant implications for future models, mechanistic interpretability, and even theorems.
The concept is still in its early stages, and the author encourages collaborative development to explore different branches of this new design axis. With potential applications in various fields, this could be an exciting direction for deep learning.
If you’re interested in learning more, there’s a blog post that explains the topic in an approachable format, along with several research papers that dive deeper into the subject.