The title here doesn't seem to match. The paper is called "TopoNets: High Performing Vision and Language Models with Brain-Like Topography"
Even with their new method, models with topography seem to perform worse than models without.
Even with their new method, models with topography seem to perform worse than models without.
Since the submitter appears to be one of the authors, maybe they can explain the connection between the two titles? (Or maybe they already have! I haven't read the entire thread)
The explanation for the original title is this plot from our publication in ICLR 2025: https://toponets.github.io/webpage_assets/FigureEfficiencyNa...
You can find more details on the website: https://toponets.github.io (see section: "Toponets deliver sparse, parameter-efficient language models")
We find out that inducing topographic structure in the weights of GPTs made them compressible (during inference) without losing out on performance.
I encourage you to revert the name if you find it justified after looking into the evidence I've shown here. Thanks.