Essays about: "Few-shot inlärning"
Showing result 1 - 5 of 6 essays containing the words Few-shot inlärning.
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1. Neuromorphic Medical Image Analysis at the Edge : On-Edge training with the Akida Brainchip
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Computed Tomography (CT) scans play a crucial role in medical imaging, allowing neuroscientists to identify intracranial pathologies such as haemorrhages and malignant tumours in the brain. This thesis explores the potential of deep learning models as an aid in intracranial pathology detection through medical imaging. READ MORE
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2. A comparative evaluation of machine learning models for engagement classification during presentations : A comparison of distance- and non-distance-based machine learning models for presentation classification and class likelihood estimation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In recent years, there has been a significant increase in the usage of audience engagement platforms, which have allowed for engaging interactions between presenters and their audiences. The increased popularity of the platforms comes from the fact that engaging and interactive presentations have been shown to improve learning outcomes and create positive presentation experiences. READ MORE
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3. Investigating Few-Shot Transfer Learning for Address Parsing : Fine-Tuning Multilingual Pre-Trained Language Models for Low-Resource Address Segmentation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Address parsing is the process of splitting an address string into its different address components, such as street name, street number, et cetera. Address parsing has been quite extensively researched and there exist some state-ofthe-art address parsing solutions, mostly unilingual. READ MORE
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4. Zero/Few-Shot Text Classification : A Study of Practical Aspects and Applications
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : SOTA language models have demonstrated remarkable capabilities in tackling NLP tasks they have not been explicitly trained on – given a few demonstrations of the task (few-shot learning), or even none at all (zero-shot learning). The purpose of this Master’s thesis has been to investigate practical aspects and potential applications of zero/few-shot learning in the context of text classification. READ MORE
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5. Anomaly Detection Across Multiple Languages
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : We present Multilingual Anomaly Detector (MAD), a toolkit to detect anomalies insensitive to the use of different languages. Unsupervised anomaly detection on high-dimensional textual data is of great relevance in both machine learning research and industrial applications. READ MORE