5 citations · 12 across the 6 of their papers we have counts for
7 papers
Evaluating the Robustness of Dense Retrievers in Interdisciplinary Domains
Sarthak Chaturvedi, Anurag Acharya, Rounak Meyur +3
Evaluation benchmark characteristics may distort the true benefits of domain adaptation in retrieval models. This creates misleading assessments that influence deployment decisions…
Randomized Algorithms for Symmetric Nonnegative Matrix Factorization
Koby Hayashi, Sinan G. Aksoy, Grey Ballard +1
Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-r…
Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs
Koby Hayashi, Sinan G. Aksoy, Haesun Park
Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering…
Hypergraph Random Walks, Laplacians, and Clustering
Koby Hayashi, Sinan G. Aksoy, Cheong Hee Park +1
We propose a flexible framework for clustering hypergraph-structured data based on recently proposed random walks utilizing edge-dependent vertex weights. When incorporating edge-d…
PLANC: Parallel Low Rank Approximation with Non-negativity Constraints
Srinivas Eswar, Koby Hayashi, Grey Ballard +3
We consider the problem of low-rank approximation of massive dense non-negative tensor data, for example to discover latent patterns in video and imaging applications. As the size…
Parallel Nonnegative CP Decomposition of Dense Tensors
Grey Ballard, Koby Hayashi, Ramakrishnan Kannan
The CP tensor decomposition is a low-rank approximation of a tensor. We present a distributed-memory parallel algorithm and implementation of an alternating optimization method for…
Shared Memory Parallelization of MTTKRP for Dense Tensors
Koby Hayashi, Grey Ballard, Jeffrey Jiang +1
The matricized-tensor times Khatri-Rao product (MTTKRP) is the computational bottleneck for algorithms computing CP decompositions of tensors. In this paper, we develop shared-memo…