3 citations · 3 across the 3 of their papers we have counts for
4 papers
Generalization in Neural Networks Through the Lens of Magnitude Potential
Sahel Torkamani, Henry Gouk, Rik Sarkar
Explaining generalization and training dynamics in neural networks remains a challenge, and various approaches have been developed to study different aspects of these phenomena. In…
Magnitude Distance: A Geometric Measure of Dataset Similarity
Sahel Torkamani, Henry Gouk, Rik Sarkar
Quantifying the distance between datasets is a fundamental question in mathematics and machine learning. We propose \textit{magnitude distance}, a novel distance metric defined on…
PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces
Wan Ki Wong, Sahel Torkamani, Michele Ciampi +1
Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often un…
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
Benedek Rozemberczki, Rik Sarkar
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce F…