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Visar resultat 1 - 5 av 13 uppsatser som matchar ovanstående sökkriterier.
1. A Study on Data-driven Methods for Selection and Evaluation of Beam Subsets in 5G NR
Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknikSammanfattning : 5G New Radio is the next generation of mobile networks and it comes with promises of ultra-high speeds, ultra-high reliability and ultra-low latency. This has posed a challenge for the engineers entrusted with the task of finding solutions which could fulfil the specification, and as a result, some promising areas have received increased attention in recent years. LÄS MER
2. Quality Attributes of Data in Distributed Deep Learning Architectures
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : Large volume of data is generated by different systems. Intelligent systems such as autonomous driving uses such large volume of data to train their artificial intelligence models. However, good quality data is one of the foremost needs of any system to function in an effective and safe manner. LÄS MER
3. Distributed Robust Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Accuracy obtained when training deep learning models with large amounts of data is high, however, training a model with such huge amounts of data on a single node is not feasible due to various reasons. For example, it might not be possible to fit the entire data set in the memory of a single node, training times can significantly increase since the dataset is huge. LÄS MER
4. Efficient serverless resource scheduling for distributed deep learning.
Master-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : Stemming from the growth and increased complexity of computer vision, natural language processing, and speech recognition algorithms; the need for scalability and fault tolerance of machine learning systems has risen. In order to comply with these demands many have turned their focus towards implementing machine learning on distributed systems. LÄS MER
5. Research on Dynamic Offloading Strategy of Satellite Edge Computing Based on Deep Reinforcement Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Nowadays more and more data is generated at the edge of the network, and people are beginning to consider decentralizing computing tasks to the edge of the network. The network architecture of edge computing is different from the traditional network architecture. LÄS MER