21 citations · 125 across the 23 of their papers we have counts for
24 papers
Capacity Analysis of Vector Symbolic Architectures
Kenneth L. Clarkson, Shashanka Ubaru, Elizabeth Yang
Hyperdimensional computing (HDC) is a biologically-inspired framework which represents symbols with high-dimensional vectors, and uses vector operations to manipulate them. The ens…
Bayesian Experimental Design for Symbolic Discovery
Kenneth L. Clarkson, Cristina Cornelio, Sanjeeb Dash +3
This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive mode…
Topological data analysis on noisy quantum computers
Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson +6
Topological data analysis (TDA) is a powerful technique for extracting complex and valuable shape-related summaries of high-dimensional data. However, the computational demands of…
Low-Rank Approximation with Matrix-Vector Products
Ainesh Bakshi, Kenneth L. Clarkson, David P. Woodruff
We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten- norm. Here, given access to a matrix through matrix-vector products, an a…
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
Cristina Cornelio, Sanjeeb Dash, Vernon Austel +6
Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. A common approach is to manually create mathematical models of natural pheno…
Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
Shashanka Ubaru, Ismail Yunus Akhalwaya, Mark S. Squillante +2
Quantum computing offers the potential of exponential speedups for certain classical computations. Over the last decade, many quantum machine learning (QML) algorithms have been pr…