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Found 14 papers, 3 papers with code
Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus
Characteristic classes, which are abstract topological invariants associated with vector bundles, have become an important notion in modern physics with surprising real-world consequences.
Understanding the Inner Workings of Language Models Through Representation Dissimilarity
As language models are applied to an increasing number of real-world applications, understanding their inner workings has become an important issue in model trust, interpretability, and transparency.
Attributing Learned Concepts in Neural Networks to Training Data
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data.
On Privileged and Convergent Bases in Neural Network Representations
Our findings give evidence that while Linear Mode Connectivity improves with increased network width, this improvement is not due to an increase in basis correlation.
How many dimensions are required to find an adversarial example?
Past work exploring adversarial vulnerability have focused on situations where an adversary can perturb all dimensions of model input.
Fast computation of permutation equivariant layers with the partition algebra
The space of permutation equivariant linear layers is a generalization of the partition algebra, an object first discovered in statistical physics with deep connections to the representation theory of the symmetric group, and the basis described above generalizes the so-called orbit basis of the partition algebra.
Edit at your own risk: evaluating the robustness of edited models to distribution shifts
The current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden.
Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension
We find that choice of prompt has a substantial impact on the intrinsic dimension of representations at both layers of the model which we explored, but that the nature of this impact depends on the layer being considered.
Experimental Observations of the Topology of Convolutional Neural Network Activations
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures.
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds
While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain.
Testing predictions of representation cost theory with CNNs
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of sensitivity to signals of different frequency.
The SVD of Convolutional Weights: A CNN Interpretability Framework
Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map.
On the Symmetries of Deep Learning Models and their Internal Representations
In this paper we seek to connect the symmetries arising from the architecture of a family of models with the symmetries of that family's internal representation of data.
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools
Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning.
