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Found 6 papers, 1 papers with code
Metric Space Magnitude for Evaluating the Diversity of Latent Representations
We develop a family of magnitude-based measures of the intrinsic diversity of latent representations, formalising a novel notion of dissimilarity between magnitude functions of finite metric spaces.
Accelerated Shapley Value Approximation for Data Evaluation
In pre-trained networks the approach is found to bring more efficiency in terms of accurate evaluation using small subsets.
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans
Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%.
Metric Space Magnitude and Generalisation in Neural Networks
Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive.
Differentially Private Shapley Values for Data Evaluation
The Shapley value has been proposed as a solution to many applications in machine learning, including for equitable valuation of data.
Euler Characteristic Surfaces
While topological data analysis of higher-dimensional parameter spaces using stronger invariants such as homology continues to be the subject of intense research, Euler characteristic is more manageable theoretically and computationally, and this analysis can be seen as an important intermediary step in multi-parameter topological data analysis.
Topological Data Analysis
Algebraic Topology
Computational Geometry
