Essays about: "Self-Supervised Learning"
Showing result 16 - 20 of 49 essays containing the words Self-Supervised Learning.
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16. Anomaly or not Anomaly, that is the Question of Uncertainty : Investigating the relation between model uncertainty and anomalies using a recurrent autoencoder approach to market time series
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Knowing when one does not know is crucial in decision making. By estimating uncertainties humans can recognize novelty both by intuition and reason, but most AI systems lack this self-reflective ability. In anomaly detection, a common approach is to train a model to learn the distinction between some notion of normal and some notion of anomalies. READ MORE
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17. Feature extraction from MEG data using self-supervised learning : Investigating contrastive representation learning methods to f ind informative representations
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Modern day society is vastly complex, with information and data constantly being posted, shared, and collected everywhere. There is often an abundance of massive amounts of unlabeled data that can not be leveraged in a supervised machine learning context. READ MORE
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18. XAI-assisted Radio Resource Management: Feature selection and SHAP enhancement
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With the fast development of radio technologies, wireless systems have become more convoluted. This complexity, accompanied by an increase of the number of connections, is translated into a need for more parameters to analyse and decisions to take at each instant. READ MORE
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19. Online Unsupervised Domain Adaptation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Learning models have seen great application in demanding tasks such as machine translation and autonomous driving. However, building such models has proved challenging, both from a computational perspective and due to the requirement of a plethora of annotated data. READ MORE
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20. Impact of model architecture and data distribution on self-supervised federated learning
University essay from Lunds universitet/Matematik LTHAbstract : Data is a crucial resource for machine learning. But in many settings, such as in healthcare or on mobile devices, there are obstacles that make it difficult to utilize the available data. This data is often distributed between many clients and private, meaning that central storage of the data is inadvisable. READ MORE