133 citations · 281 across the 44 of their papers we have counts for
5 papers · 2 filters
What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes
Victor Lecomte, Kushal Thaman, Rylan Schaeffer +3
Polysemantic neurons -- neurons that activate for a set of unrelated features -- have been seen as a significant obstacle towards interpretability of task-optimized deep networks,…
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells
Rylan Schaeffer, Mikail Khona, Tzuhsuan Ma +3
To solve the spatial problems of mapping, localization and navigation, the mammalian lineage has developed striking spatial representations. One important spatial representation is…
Deceptive Alignment Monitoring
Andres Carranza, Dhruv Pai, Rylan Schaeffer +2
As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the mo…
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
Dhruv Pai, Andres Carranza, Rylan Schaeffer +2
We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks. Its primary goal is advancing the…
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
Rylan Schaeffer, Mikail Khona, Zachary Robertson +5
Double descent is a surprising phenomenon in machine learning, in which as the number of model parameters grows relative to the number of data, test error drops as models grow ever…