Revamping Neural Topology: The Cost of Precision
New techniques in neural topology offer a significant speed advantage over traditional grid-based methods, but they come with a new set of challenges. Is the trade-off worth it?
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New preprint:
New preprint: Random network structure stabilizes neural manifolds We’re excited to share our new work on representational drift. doi.org/10
Topological Neural Operators [deep math version]
Authors: Lennart Bastian, Samuel Leventhal, Mustafa Hajij, Tolga Birdal
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Wider Neural Networks with Fewer Parameters Improve Performance by Reducing Feature Interference
This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves
arxiv.org·1mo ago
mHC: A Manifold-Constrained Framework to Stabilize and Scale Hyper-Connections in Neural Networks
Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past
arxiv.org·6mo ago
NEvo: Neural-Guided Evolutionary Video Synthesis
am i missing something or is there actually no validation that this is actually driving neural activation as predicted? there is no actual i
nevo-project.epfl.ch·18h agoNEvo: Neural-Guided Evolutionary Video Synthesis
am i missing something or is there actually no validation that this is actually driving neural activation as predicted? there is no actual i
nevo-project.epfl.ch·18h ago
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