24 citations · 40 across the 10 of their papers we have counts for
4 papers · 1 filter
Context is All You Need
Jean Erik Delanois, Shruti Joshi, Ryan Golden +2
Artificial Neural Networks (ANNs) are increasingly deployed across diverse real-world settings, where they must operate under data distributions that differ from those seen during…
Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
Vitória Barin Pacela, Vitória Barin-Pacela, Shruti Joshi +3
The linear representation hypothesis states that neural network activations encode high-level concepts as linear mixtures. However, under superposition, this encoding is a projecti…
Causality is Key for Interpretability Claims to Generalise
Shruti Joshi, Aaron Mueller, David Klindt +3
Interpretability research on large language models (LLMs) has yielded important insights into model behaviour, yet recurring pitfalls persist: findings that do not generalise, and…
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
Shruti Joshi, Théo Saulus, Wieland Brendel +3
Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth factors. These metrics…