10 citations · 24 across the 8 of their papers we have counts for
3 papers · 1 filter
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Atticus Geiger, Zhengxuan Wu, Christopher Potts +2
Causal abstraction is a promising theoretical framework for explainable artificial intelligence that defines when an interpretable high-level causal model is a faithful simplificat…
Causal Abstractions of Neural Networks
Atticus Geiger, Hanson Lu, Thomas Icard +1
Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method ground…
Preferential Structures for Comparative Probabilistic Reasoning
Matthew Harrison-Trainor, Wesley H. Holliday, Thomas F. Icard
Qualitative and quantitative approaches to reasoning about uncertainty can lead to different logical systems for formalizing such reasoning, even when the language for expressing u…