5,337
edits
Deep Learning: Difference between revisions
no edit summary
No edit summary |
|||
| Line 238: | Line 238: | ||
<ref name="liu2020towards">Chaoyue Liu, Libin Zhu, Mikhail Belkin (2020). Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning [https://arxiv.org/abs/2003.00307 https://arxiv.org/abs/2003.00307]</ref> | <ref name="liu2020towards">Chaoyue Liu, Libin Zhu, Mikhail Belkin (2020). Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning [https://arxiv.org/abs/2003.00307 https://arxiv.org/abs/2003.00307]</ref> | ||
<ref name="du2019gradient">Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh (2019). Gradient Descent Provably Optimizes Over-parameterized Neural Networks (ICLR 2019) [https://arxiv.org/abs/1810.02054 https://arxiv.org/abs/1810.02054]</ref> | <ref name="du2019gradient">Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh (2019). Gradient Descent Provably Optimizes Over-parameterized Neural Networks (ICLR 2019) [https://arxiv.org/abs/1810.02054 https://arxiv.org/abs/1810.02054]</ref> | ||
<ref name="soudry2018implicit">Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro (2018) The Implicit Bias of Gradient Descent on Separable Data ''The Journal of Machine Learning Research'' 2018 [https://arxiv.org/abs/1710.10345 https://arxiv.org/abs/1710.10345]</ref> | |||
}} | }} | ||