Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 80 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 33 tok/s Pro
GPT-5 High 25 tok/s Pro
GPT-4o 117 tok/s Pro
Kimi K2 176 tok/s Pro
GPT OSS 120B 457 tok/s Pro
Claude Sonnet 4.5 32 tok/s Pro
2000 character limit reached

Backprop Evolution (1808.02822v1)

Published 8 Aug 2018 in cs.NE, cs.LG, and stat.ML

Abstract: The back-propagation algorithm is the cornerstone of deep learning. Despite its importance, few variations of the algorithm have been attempted. This work presents an approach to discover new variations of the back-propagation equation. We use a domain specific lan- guage to describe update equations as a list of primitive functions. An evolution-based method is used to discover new propagation rules that maximize the generalization per- formance after a few epochs of training. We find several update equations that can train faster with short training times than standard back-propagation, and perform similar as standard back-propagation at convergence.

Citations (9)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.