Essays about: "Active deep dropout"

Found 3 essays containing the words Active deep dropout.

1. 1. Active Learning for Extractive Question Answering

   University essay from Linköpings universitet/Statistik och maskininlärning

   Author : Salvador Marti Roman; [2022]
   Keywords : Machine Learning; Deep Learning; Active Learning; Natural Language Processing; NLP; Question Answering; Transformers; Uncertainty; Language Models;

   Abstract : Data labelling for question answering tasks (QA) is a costly procedure that requires oracles to read lengthy excerpts of texts and reason to extract an answer for a given question from within the text. QA is a task in natural language processing (NLP), where a majority of recent advancements have come from leveraging the vast corpora of unlabelled and unstructured text available online. READ MORE

3. 2. Driver Behavior Classification in Electric Vehicles

   University essay from Göteborgs universitet/Institutionen för data- och informationsteknik

   Author : FEDERICA COMUNI; CHRISTOPHER MÉSZÁROS; [2021-07-06]
   Keywords : Aggressive driver behavior; Driver behavior classification; Self-attention; Recurrence plots; active learning; Active deep dropout; Gradual pseudo labeling;

   Abstract : Studies have shown that driving style affects the energy consumption of electric vehicles, with aggressive driving consuming up to 30% more energy than moderate driving. Therefore, modeling of aggressive driving can provide a more precise estimation of the energy consumption and the remaining range of a vehicle. READ MORE

4. 3. Active Learning for Road Segmentation using Convolutional Neural Networks

   University essay from Linköpings universitet/Datorseende

   Author : Michael Sörsäter; [2018]
   Keywords : Active learning; Monte Carlo dropout; Semantic Segmentation; Convolutional Neural Networks; Uncertainty; CNN; Deep Learning; Road Segmentation; Machine learning;

   Abstract : In recent years, development of Convolutional Neural Networks has enabled high performing semantic segmentation models. Generally, these deep learning based segmentation methods require a large amount of annotated data. Acquiring such annotated data for semantic segmentation is a tedious and expensive task. READ MORE