Sökning: "Deep mixing"
Visar resultat 1 - 5 av 34 uppsatser innehållade orden Deep mixing.
1. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder
Kandidat-uppsats, Lunds universitet/Fysiska institutionenSammanfattning : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. LÄS MER
2. Magmatic processes and storage beneath Heard Island, southern Indian Ocean
Master-uppsats, Uppsala universitet/Institutionen för geovetenskaperSammanfattning : A young marine island called Heard Island is located in the southern Kerguelen Plateau in the Indian Ocean, a large igneous province created by the Kerguelen mantle plume. The two major geographic regions on Heard Island have two principal volcano-magmatic suites. LÄS MER
3. Learning with Synthetically Blocked Images for Sensor Blockage Detection
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : With the increasing demand for labeled data in machine learning for visual perception tasks, the interest in using synthetically generated data has grown. Due to the existence of a domain gap between synthetic and real data, strategies in domain adaptation are necessary to achieve high performance with models trained on synthetic or mixed data. LÄS MER
4. Finite Element Modeling of Installation Effects of Soil-Cement Columns
Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för samhällsbyggnad och naturresurserSammanfattning : Since the 1970's deep mixing columns have been widely used all over the world to improve the performance of soft soil in regard to bearing capacity or deformation behaviour. They are installed by mixing a binding agent, e.g. cement, in situ with the soil. LÄS MER
5. Semantic segmentation of off-road scenery on embedded hardware using transfer learning
Master-uppsats, KTH/MekatronikSammanfattning : Real-time semantic scene understanding is a challenging computer vision task for autonomous vehicles. A limited amount of research has been done regarding forestry and off-road scene understanding, as the industry focuses on urban and on-road applications. LÄS MER