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Optuna: A Next-generation Hyperparameter Optimization Framework

25 Jul 2019pfnet/optuna

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.

DISTRIBUTED COMPUTING HYPERPARAMETER OPTIMIZATION

MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales

20 Oct 2018Azure/mmlspark

We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation.

DISTRIBUTED COMPUTING OBJECT DETECTION

Flexible and Scalable Deep Learning with MMLSpark

11 Apr 2018Azure/mmlspark

In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark.

DISTRIBUTED COMPUTING

Billion-scale Network Embedding with Iterative Random Projection

7 May 2018benedekrozemberczki/karateclub

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

DISTRIBUTED COMPUTING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

25 Mar 2020uber/fiber

Recent advances in machine learning are consistently enabled by increasing amounts of computation.

DISTRIBUTED COMPUTING

A System for Massively Parallel Hyperparameter Tuning

ICLR 2018 c-bata/goptuna

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

DISTRIBUTED COMPUTING HYPERPARAMETER OPTIMIZATION

MANGO: A Python Library for Parallel Hyperparameter Tuning

22 May 2020ARM-software/mango

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually.

DISTRIBUTED COMPUTING DISTRIBUTED OPTIMIZATION HYPERPARAMETER OPTIMIZATION

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

7 Nov 2016gingsmith/cocoa

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

DISTRIBUTED COMPUTING DISTRIBUTED OPTIMIZATION

Distributed Deep Neural Networks over the Cloud, the Edge and End Devices

6 Sep 2017kunglab/ddnn

In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.

DISTRIBUTED COMPUTING OBJECT RECOGNITION SENSOR FUSION