REVIEW 2 cited by
Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Many supervised machine learning methods are naturally cast as optimization problems. For prediction models which are linear in their parameters, this often leads to convex problems for which many mathematical guarantees exist. Models which are non-linear in their parameters such as neural networks lead to non-convex optimization problems for which guarantees are harder to obtain. In this review paper, we consider two-layer neural networks with homogeneous activation functions where the number of hidden neurons tends to infinity, and show how qualitative convergence guarantees may be derived.
Forward citations
Cited by 2 Pith papers
-
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
In the teacher-student setting, variable-projection training of two-layer networks is shown to match a weighted ultra-fast diffusion in the zero-regularization limit, giving linear convergence of the learned feature d...
-
Trajectory inference via Acceleration Matching
Acceleration Matching is a simulation-free, flow-matching-style algorithm for multi-marginal trajectory inference that regresses onto an explicit kinetic Brownian bridge acceleration field in phase space.
Discussion (0). Continue with ORCID to comment.