Distributed Computing

65 papers with code • 0 benchmarks • 1 datasets

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Use these libraries to find Distributed Computing models and implementations
2 papers
2 papers


Most implemented papers

Optuna: A Next-generation Hyperparameter Optimization Framework

pfnet/optuna 25 Jul 2019

We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications.

A System for Massively Parallel Hyperparameter Tuning

liamcli/darts_asha ICLR 2018

Modern learning models are characterized by large hyperparameter spaces and long training times.

FedML: A Research Library and Benchmark for Federated Machine Learning

FedML-AI/FedML 27 Jul 2020

Federated learning (FL) is a rapidly growing research field in machine learning.

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

gingsmith/cocoa 7 Nov 2016

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.

Billion-scale Network Embedding with Iterative Random Projection

ZW-ZHANG/RandNE 7 May 2018

Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.

Orchestral: a lightweight framework for parallel simulations of cell-cell communication

Aratz/orchestral 28 Jun 2018

By the use of operator-splitting we decouple the simulation of reaction-diffusion kinetics inside the cells from the simulation of molecular cell-cell interactions occurring on the boundaries between cells.

Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering

zhanglabtools/dbmd 10 Feb 2020

Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system.

Computing High Accuracy Power Spectra with Pico

marius311/pypico 2 Dec 2007

This paper presents the second release of Pico (Parameters for the Impatient COsmologist).

Online Asynchronous Distributed Regression

ryadzenine/dolphin 16 Jul 2014

Distributed computing offers a high degree of flexibility to accommodate modern learning constraints and the ever increasing size of datasets involved in massive data issues.

MLitB: Machine Learning in the Browser

software-engineering-amsterdam/MLitB 8 Dec 2014

Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large.