Smoothing methods for nonsmooth, nonconvex minimization

By Xiaojun Chen. Mathematical programming, 2012

348 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/3bdf3d7e6e212dee4c8c95c5ecdd6111fc5ee42b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yoshua Bengio

In Computational Mechanics

1. Yoshua Bengio
2. Qixiang Ye
3. Caglar Gulcehre
4. Marcin Moczulski
5. Chunyuan Li
6. Dougal Maclaurin
7. Hugo Larochelle
8. Jitendra Malik

In Mathematical Physics

1. Qixiang Ye
2. Yoshua Bengio
3. Francesco Visin

Citations in the corpus

• C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection (2019, background): “Gradient-based optimization over a sequence of mollified loss functions has been shown converging to stronger global minima [10].”
• Mollifying Networks (2016, background): “Gradient-based optimization over a sequence of mollified objective functions has been shown to converge (Chen, 2012).”