Pith. sign in

REVIEW

High-Dimensional Non-Convex Landscapes and Gradient Descent Dynamics

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

arxiv 2308.03754 v2 pith:XRS63NNC submitted 2023-08-07 cond-mat.dis-nn cond-mat.stat-mech

classification cond-mat.dis-nncond-mat.stat-mech
keywords dynamicshigh-dimensionalphysicsdescentdevelopedgradientlandscapesnon-convex
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Our aim is to show how approaches developed in physics, mainly statistical physics of disordered systems, can be used to tackle open questions on high-dimensional dynamics in Machine Learning.

Discussion (0). Continue with ORCID to comment.

Pith tools