Web: http://arxiv.org/abs/2102.00485

Jan. 28, 2022, 2:11 a.m. | Stefan Horoi, Jessie Huang, Bastian Rieck, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy

cs.LG updates on arXiv.org arxiv.org

Recent work has established clear links between the generalization
performance of trained neural networks and the geometry of their loss landscape
near the local minima to which they converge. This suggests that qualitative
and quantitative examination of the loss landscape geometry could yield
insights about neural network generalization performance during training. To
this end, researchers have proposed visualizing the loss landscape through the
use of simple dimensionality reduction techniques. However, such visualization
methods have been limited by their linear nature …

arxiv geometry network neural neural network

More from arxiv.org / cs.LG updates on arXiv.org

Director, Data Science (Advocacy & Nonprofit)

@ Civis Analytics | Remote

Data Engineer

@ Rappi | [CO] Bogotá

Data Scientist V, Marketplaces Personalization (Remote)

@ ID.me | United States (U.S.)

Product OPs Data Analyst (Flex/Remote)

@ Scaleway | Paris

Big Data Engineer

@ Risk Focus | Riga, Riga, Latvia

Internship Program: Machine Learning Backend

@ Nextail | Remote job