Search Results for author:
Found 18 papers, 2 papers with code
Capacity Analysis of Vector Symbolic Architectures
The ensemble of a particular vector space and a prescribed set of vector operations (including one addition-like for "bundling" and one outer-product-like for "binding") form a *vector symbolic architecture* (VSA).
Towards Quantum Advantage on Noisy Quantum Computers
TDA is a powerful technique for extracting complex and valuable shape-related summaries of high-dimensional data.
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
In this paper, we present a novel surrogate model for representation learning and uncertainty quantification, which aims to deal with data of moderate to high dimensions.
Efficient Scaling of Dynamic Graph Neural Networks
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems.
Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
In this paper, we completely overhaul the QTDA algorithm to achieve an improved exponential speedup and depth complexity of $O(n\log(1/(\delta\epsilon)))$.
Projection techniques to update the truncated SVD of evolving matrices
This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time.
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription
Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription.
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems, which can then be solved independently via the grouping approach.
Dynamic Graph Convolutional Networks Using the Tensor M-Product
In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes
These approaches are supervised; a predictive task with ground-truth labels is used to drive the learning.
Find the dimension that counts: Fast dimension estimation and Krylov PCA
In this paper, we consider the problem of simultaneously estimating the dimension of the principal (dominant) subspace of these covariance matrices and obtaining an approximation to the subspace.
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss.
Sampling and multilevel coarsening algorithms for fast matrix approximations
To tackle these problems, we consider algorithms that are based primarily on coarsening techniques, possibly combined with random sampling.
Multilabel Classification with Group Testing and Codes
In this work, we propose a novel approach based on group testing to solve such large multilabel classification problems with sparse label vectors.
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications.
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness
We thus effectively compute a histogram of the spectrum, which can stand in for the true singular values in many applications.
Fast estimation of approximate matrix ranks using spectral densities
In this paper, we present two computationally inexpensive techniques to estimate the approximate ranks of such large matrices.
Low rank approximation and decomposition of large matrices using error correcting codes
In this paper, we show how matrices from error correcting codes can be used to find such low rank approximations and matrix decompositions, and extend the framework to linear least squares regression problems.
