Cloud Computing
90 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Cloud Computing models and implementationsMost implemented papers
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
We propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization.
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
harveyslash/Deep-Steganography
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Journal of Cloud Computing 2022
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
chrieke/awesome-satellite-imagery-datasets
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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
hfp/libxsmm
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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
galdl/rl_delay_basic
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ICLR 2021
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.
HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud.
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.
Satellite Image Time Series Analysis for Big Earth Observation Data
e-sensing/sits
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Solutions that are efficient for specific hardware architectures can not be used in other environments.
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning
andreareds/towardsuncertaintyawareworkloadprediction
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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.
