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Towards Combinatorial Interpretability of Neural Computation

10 April 2025

Papers citing "Towards Combinatorial Interpretability of Neural Computation"

20 / 20 papers shown

Title
From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit Valérie Costa Thomas Fel Ekdeep Singh Lubana Bahareh Tolooshams Demba Ba 69 0 0 03 Jun 2025
Do Sparse Autoencoders Generalize? A Case Study of Answerability Lovis Heindrich Philip Torr Fazl Barez Veronika Thost 142 2 0 27 Feb 2025
On the Complexity of Neural Computation in Superposition Micah Adler Nir Shavit 231 4 0 05 Sep 2024
Mathematical Models of Computation in Superposition Kaarel Hänni Jake Mendel Dmitry Vaintrob Lawrence Chan SupR 92 11 0 10 Aug 2024
Validating Mechanistic Interpretations: An Axiomatic Approach Nils Palumbo Ravi Mangal Zifan Wang Saranya Vijayakumar Corina S. Pasareanu Somesh Jha 106 1 0 18 Jul 2024
Transcoders Find Interpretable LLM Feature Circuits Jacob Dunefsky Philippe Chlenski Neel Nanda 85 34 0 17 Jun 2024
Automatically Identifying Local and Global Circuits with Linear Computation Graphs Xuyang Ge Fukang Zhu Wentao Shu Junxuan Wang Zhengfu He Xipeng Qiu 96 10 0 22 May 2024
Improving Dictionary Learning with Gated Sparse Autoencoders Senthooran Rajamanoharan Arthur Conmy Lewis Smith Tom Lieberum Vikrant Varma János Kramár Rohin Shah Neel Nanda RALM 82 94 0 24 Apr 2024
What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes Victor Lecomte Kushal Thaman Rylan Schaeffer Naomi Bashkansky Trevor Chow Sanmi Koyejo AAML MILM 71 12 0 05 Dec 2023
Sparse Autoencoders Find Highly Interpretable Features in Language Models Hoagy Cunningham Aidan Ewart Logan Riggs R. Huben Lee Sharkey MILM 143 449 0 15 Sep 2023
Progress measures for grokking via mechanistic interpretability Neel Nanda Lawrence Chan Tom Lieberum Jess Smith Jacob Steinhardt 115 451 0 12 Jan 2023
Polysemanticity and Capacity in Neural Networks Adam Scherlis Kshitij Sachan Adam Jermyn Joe Benton Buck Shlegeris MILM 240 32 0 04 Oct 2022
Toy Models of Superposition Nelson Elhage Tristan Hume Catherine Olsson Nicholas Schiefer T. Henighan ... Sam McCandlish Jared Kaplan Dario Amodei Martin Wattenberg C. Olah AAML MILM 242 380 0 21 Sep 2022
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 673 4,945 0 23 Jan 2020
A Constructive Prediction of the Generalization Error Across Scales Jonathan S. Rosenfeld Amir Rosenfeld Yonatan Belinkov Nir Shavit 113 215 0 27 Sep 2019
Similarity of Neural Network Representations Revisited Simon Kornblith Mohammad Norouzi Honglak Lee Geoffrey E. Hinton 188 1,441 0 01 May 2019
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks Anh Totti Nguyen J. Yosinski Jeff Clune 120 330 0 11 Feb 2016
Deep Learning and the Information Bottleneck Principle Naftali Tishby Noga Zaslavsky DRL 270 1,600 0 09 Mar 2015
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 343 7,340 0 20 Dec 2013
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 336 12,496 0 24 Jun 2012