Distributed Computing
55 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Libraries
Use these libraries to find Distributed Computing models and implementations
MNIST-8M
Most implemented papers
Optuna: A Next-generation Hyperparameter Optimization Framework
pfnet/optuna
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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
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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
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Federated learning (FL) is a rapidly growing research field in machine learning.
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
Billion-scale Network Embedding with Iterative Random Projection
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
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
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
This paper presents the second release of Pico (Parameters for the Impatient COsmologist).
Online Asynchronous Distributed Regression
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
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.
