Cloud Computing
82 papers with code • 0 benchmarks • 0 datasets
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Agnostic Federated Learning
A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.
A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system
The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning.
SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from multi-sensor remote sensing imagery.
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks.
Acting in Delayed Environments with Non-Stationary Markov Policies
We introduce a framework for learning and planning in MDPs where the decision-maker commits actions that are executed with a delay of $m$ steps.
VMAgent: Scheduling Simulator for Reinforcement Learning
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning
Results show that modelling the uncertainty of predictions has a positive impact on performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications.
Large-scale Artificial Neural Network: MapReduce-based Deep Learning
Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload.
CLARIN-EL Web-based Annotation Tool
This paper presents a new Web-based annotation tool, the {``}CLARIN-EL Web-based Annotation Tool{''}.
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results.