Oymak, Samet, and Soltanolkotabi, Mahdi. Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks. Retrieved from https://par.nsf.gov/biblio/10200049. IEEE Journal on Selected Areas in Information Theory 1.1 Web. doi:10.1109/JSAIT.2020.2991332.

Oymak, Samet, & Soltanolkotabi, Mahdi. Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks. IEEE Journal on Selected Areas in Information Theory, 1 (1). Retrieved from https://par.nsf.gov/biblio/10200049. https://doi.org/10.1109/JSAIT.2020.2991332

Oymak, Samet, and Soltanolkotabi, Mahdi. "Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks". IEEE Journal on Selected Areas in Information Theory 1 (1). Country unknown/Code not available. https://doi.org/10.1109/JSAIT.2020.2991332. https://par.nsf.gov/biblio/10200049.

@article{osti_10200049,
place = {Country unknown/Code not available}, title = {Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks}, url = {https://par.nsf.gov/biblio/10200049}, DOI = {10.1109/JSAIT.2020.2991332}, abstractNote = {}, journal = {IEEE Journal on Selected Areas in Information Theory}, volume = {1}, number = {1}, author = {Oymak, Samet and Soltanolkotabi, Mahdi}, editor = {null} }