Sökning: "Few-shot inlärning"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Few-shot inlärning.

1. 1. Neuromorphic Medical Image Analysis at the Edge : On-Edge training with the Akida Brainchip

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ebba Bråtman; Lucas Dow; [2023]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

3. 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

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Rebwar Ali Omer Bajallan; [2022]
   Nyckelord :Audience engagement; Engagement quantification; Machine learning; Distance-based models; Few-shot learning; Statistical analysis; Publiksengagemang; Engagemangskvantifiering; Maskininlärning; Distansbaserade modeller; Few-shot inlärning; Statistisk analys;

   Sammanfattning : 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. LÄS MER

4. 3. Investigating Few-Shot Transfer Learning for Address Parsing : Fine-Tuning Multilingual Pre-Trained Language Models for Low-Resource Address Segmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Hrafndís Heimisdóttir; [2022]
   Nyckelord :Address Parsing; Address Segmentation; Few-Shot Learning; Transfer Learning; Named Entity Recognition; Adressavkodning; Adress Segmentering; Inlärning med Få Exempel; Överföringsinlärning;

   Sammanfattning : 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. LÄS MER

5. 4. Zero/Few-Shot Text Classification : A Study of Practical Aspects and Applications

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jacob Åslund; [2021]
   Nyckelord :zero-shot learning; few-shot learning; text classification; active learning; automated data labeling; interpretable machine learning; deep learning; NLP; NLU; zero-shot learning; few-shot learning; textklassificering; aktiv inlärning; automatiserad datamärkning; tolkningsbar maskininlärning; djupinlärning; NLP; NLU;

   Sammanfattning : 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. LÄS MER

7. 5. Anomaly Detection Across Multiple Languages

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mastafa Foufa; [2020]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER