This portal has been archived. Explore the next generation of this technology.
Thresholds of descending algorithms in inference problems
lib:d9d204de18fbc933 (v1.0.0)
Authors: Stefano Sarao Mannelli,Lenka Zdeborova
ArXiv: 2001.00479
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/2001.00479v2
We review recent works on analyzing the dynamics of gradient-based algorithms in a prototypical statistical inference problem. Using methods and insights from the physics of glassy systems, these works showed how to understand quantitatively and qualitatively the performance of gradient-based algorithms. Here we review the key results and their interpretation in non-technical terms accessible to a wide audience of physicists in the context of related works.
ArXiv: 2001.00479
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/2001.00479v2
We review recent works on analyzing the dynamics of gradient-based algorithms in a prototypical statistical inference problem. Using methods and insights from the physics of glassy systems, these works showed how to understand quantitatively and qualitatively the performance of gradient-based algorithms. Here we review the key results and their interpretation in non-technical terms accessible to a wide audience of physicists in the context of related works.
Relevant initiatives
Related knowledge about this paper
Reproduced results (crowd-benchmarking and competitions)
Artifact and reproducibility checklists
- ACM/cTuning artifact appendix and reproducibility checklist
- NeurIPS reproducibility checklist
- SIGPLAN's checklist for empirical evaluation
- Collective Knowledge (organizing research projects based on FAIR principles)