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computer-code-3

Cloud Computing

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in cloud-computing-3

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Libraries

Use these libraries to find cloud-computing-3 models and implementations

dmlc/xgboost

2 papers 25,528

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No datasets available.

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Most implemented papers

Agnostic Federated Learning

M. Mohri, A. Suresh, Gary Sivek•Thu Jan 31 2019

This work proposes a new framework of agnostic federated learning, where the centralized model is optimized for any target distribution formed by a mixture of the client distributions, and shows that this framework naturally yields a notion of fairness.

1063

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

imperial-qore/COSCO

2 papers 71

Paper Graph

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

Ahmed A. Mawgoud, M. Taha, Amr Abu-Talleb, A. Kotb•Wed Nov 30 2022

The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-Hoc cloud system environment.

12 0

Paper Graph

Acting in Delayed Environments with Non-Stationary Markov Policies

Shie Mannor, E. Derman, Gal Dalal•Wed Jan 27 2021

This work introduces a framework for learning and planning in MDPs where the decision-maker commits actions that are executed with a delay of m steps, and proves that with execution delay, Markov policies in the original state-space are sufficient for attaining maximal reward, but need to be non-stationary.

39 0

Paper Graph

VMAgent: Scheduling Simulator for Reinforcement Learning

Xiangfeng Wang, Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, Yiqiu Hu, Lei Zhu, Qian Peng, Hong Zha•Wed Dec 08 2021

A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling, and also helps to explore better learning-based scheduling solutions.

3 0

Paper Graph

Large-scale Artificial Neural Network: MapReduce-based Deep Learning

Risheng Wang, Kairan Sun, Xu Wei, Gengtao Jia, Ruizhi Li•Thu Oct 08 2015

A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data.

10 0

Paper Graph

Forecasting workload in cloud computing: towards uncertainty-aware predictions and transfer learning

Andrea Rossi, Andrea Visentin, Diego Carraro, Steven D. Prestwich, Kenneth N. Brown•Mon Feb 24 2025

Results show that modelling the uncertainty of predictions positively impacts performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications.

9 0

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DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

Chang Liu, Yu Cao, Yan Luo, Guanling Chen, V. Vokkarane, Yunsheng Ma•Tue May 24 2016

A new Convolutional Neural Network CNN-based food image recognition algorithm is proposed to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices e.g., smartphone.

274 0

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Cloud Computing | State-of-the-Art