Notes on the Origin of Implicit Regularization in SGD
Read OriginalThis article analyzes the concept of implicit regularization in Stochastic Gradient Descent (SGD), explaining how the optimization algorithm itself biases learning toward minima that generalize well. It discusses recent research moving beyond the neural tangent kernel framework to study SGD with finite learning rates and minibatches, providing a more practical view of why deep neural networks generalize effectively.
Comments
No comments yet
Be the first to share your thoughts!
Browser Extension
Get instant access to AllDevBlogs from your browser
Top of the Week
1
2
Using Browser Apis In React Practical Guide
Jivbcoop
•
2 votes
3
Better react-hook-form Smart Form Components
Maarten Hus
•
2 votes
4
Top picks — 2026 January
Paweł Grzybek
•
1 votes
5
In Praise of –dry-run
Henrik Warne
•
1 votes
6
Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years On
Ferenc Huszár
•
1 votes
7
Vibe coding your first iOS app
William Denniss
•
1 votes
8
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook
•
1 votes
9
Quoting Thariq Shihipar
Simon Willison
•
1 votes
10
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft
•
1 votes