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Neural Turing Machines

Creator
Creator
Seonglae Cho
Created
Created
2025 Feb 4 14:49
Editor
Editor
Seonglae Cho
Edited
Edited
2025 Feb 4 14:51
Refs
Refs
AI Circuit
 
 
 
 
 

Finite State Automata

Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
Mechanistic interpretability seeks to understand neural networks by breaking them into components that are more easily understood than the whole. By understanding the function of each component, and how they interact, we hope to be able to reason about the behavior of the entire network. The first step in that program is to identify the correct components to analyze.
Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
https://transformer-circuits.pub/2023/monosemantic-features#phenomenology-fsa

Neural Turing Machine

arxiv.org
https://arxiv.org/pdf/1410.5401
RNN with
Attention Mechanism
notion image
Attention and Augmented Recurrent Neural Networks
A visual overview of neural attention, and the powerful extensions of neural networks being built on top of it.
https://distill.pub/2016/augmented-rnns/
Attention and Augmented Recurrent Neural Networks
 
 

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Neural Turing Machines
Copyright Seonglae Cho