1 citations · 1 across the 5 of their papers we have counts for
8 papers
Complete or Sparse: A Tale of Two Identifiabilities
Junze Zhou, David Klindt
Understanding when neural networks learn interpretable representations is central to mechanistic interpretability. We study this through identifiability: when can supervised learni…
Inference and learning in sparse autoencoders as natural gradient flow
Hadi Vafaii, Tejas Rao, David Chanin +6
Sparse autoencoders are widely used to uncover interpretable features in neural networks, yet reliable recovery remains difficult when features overlap or activate infrequently. Th…
Data Whitening Improves Sparse Autoencoder Learning
Ashwin Saraswatula, David Klindt
Sparse autoencoders (SAEs) have emerged as a promising approach for learning interpretable features from neural network activations. However, the optimization landscape for SAE tra…
From superposition to sparse codes: interpretable representations in neural networks
David Klindt, Charles O'Neill, Patrik Reizinger +2
Understanding how information is represented in neural networks is a fundamental challenge in both neuroscience and artificial intelligence. Despite their nonlinear architectures,…
Latent computing by biological neural networks: A dynamical systems framework
Fatih Dinc, Marta Blanco-Pozo, David Klindt +8
Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable acr…
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
Charles O'Neill, Alim Gumran, David Klindt
A recent line of work has shown promise in using sparse autoencoders (SAEs) to uncover interpretable features in neural network representations. However, the simple linear-nonlinea…
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems
David Klindt, Sophia Sanborn, Francisco Acosta +2
Single neurons in neural networks are often interpretable in that they represent individual, intuitively meaningful features. However, many neurons exhibit $\textit{mixed selectivi…
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
Adele Myers, Saiteja Utpala, Shubham Talbar +25
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learn…