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An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation

An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation

Ahmed Abdulaal
Hugo Fry
Nina Montaña-Brown
Ayodeji Ijishakin
Jack Gao
Stephanie L. Hyland
Daniel C. Alexander
Daniel Coelho De Castro
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Papers citing "An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation"

3 / 3 papers shown
Title
Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition
Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition
Zhengfu He
J. Wang
Rui Lin
Xuyang Ge
Wentao Shu
Qiong Tang
Junzhe Zhang
Xipeng Qiu
70
0
0
29 Apr 2025
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
Bartosz Cywiñski
Kamil Deja
DiffM
63
6
0
29 Jan 2025
A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
David Chanin
James Wilken-Smith
Tomáš Dulka
Hardik Bhatnagar
Joseph Bloom
15
17
0
22 Sep 2024