Summary
A user on r/ML is seeking clarification on the correct implementation of backpropagation in Siamese networks, noting that original paper explanations are insufficient. They are comparing two potential approaches: processing inputs sequentially with a single weight update, versus simultaneously using two copies of the network (like a Bi-encoder).
Editorial note
AI Dose summarizes public reporting and links to original sources when they are available. Review the Editorial Policy, Disclaimer, or Contact page if you need to flag a correction or understand how this site handles sources.
Continue Reading
Explore related coverage about community news and adjacent AI developments: [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT, [r/LocalLLaMA] karpathy / autoresearch, [r/ML] KIV: 1M token context window on a RTX 4070 (12GB VRAM), no retraining, drop-in HuggingFace cache replacement - Works with any model that uses DynamicCache [P], [r/ML] LLMs learn backwards, and the scaling hypothesis is bounded. [D].
Related Articles
- [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT
March 29, 2026
- [r/LocalLLaMA] karpathy / autoresearch
March 10, 2026
- [r/ML] KIV: 1M token context window on a RTX 4070 (12GB VRAM), no retraining, drop-in HuggingFace cache replacement - Works with any model that uses DynamicCache [P]
April 13, 2026
- [r/ML] LLMs learn backwards, and the scaling hypothesis is bounded. [D]
April 12, 2026
Next read
[r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT
Stay with the thread by reading one adjacent story before leaving this update.
Comments
Sign in to leave a comment.