Search · four archives
Search · four archives
8 papers · ranked by Valyu relevance
Artur Czumaj, Peter Davies-Peck, Merav Parter
In this paper, we study the power and limitations of component-stable algorithms in the low-space model of massively parallel computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) introduced the class of component-stable low-space MPC algorithms, which are, informally, those algorithms for which the outputs…
Matthias Volk, Borzoo Bonakdarpour, Joost-Pieter Katoen, Saba Aflaki
Randomization is a key concept in distributed computing to tackle impossibility results. This also holds for self-stabilization in anonymous networks where coin flips are often used to break symmetry. Although the use of randomization in self-stabilizing algorithms is rather common, it is unclear what the optimal coin…
Derya Malak, Mohammad Reza Deylam Salehi, Berksan Serbetci, Petros Elia + 2 more
'Petros Elia' 'Chintha Tellambura' 'Jun Chen'] The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably…
Yongcheng Yang, Yifei Huang, Xiaohuan Qin, Shenglian Lu + 3 more
'Yanlin Geng' 'Youlong Wu' 'Ling Liu'] Coded distributed computing (CDC) is a powerful approach to reduce the communication overhead in distributed computing frameworks by utilizing coding techniques. In this paper, we focus on the CDC problem in $(H,L)$-combination networks, where H APs act as intermediate pivots and…
Abdullah Al Nahid, Linda Serafin, Nicholas Mancuso
In many applications, from statistical inference to machine learning, calculating the trace of a matrix is a fundamental operation, yet may be infeasible due to memory constraints. Stochastic trace estimation offers a practical solution by using randomized matrix-vector products to obtain accurate, unbiased estimates…
J Kyle Medley, Shaik Asifullah, Joseph Hellerstein, Herbert M Sauro
Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non–convex, non–linear optimization problem. Many algorithms have been proposed…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…