Sökning: "Residual Networks"
Visar resultat 1 - 5 av 40 uppsatser innehållade orden Residual Networks.
1. Room Impulse Response Interpolation
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In Virtual Reality (VR) systems, the incorporation of acoustics allows for the generation of audio-visual stimuli, facilitating applications in engineering, architecture, and design. The goal of virtual acoustics is to create a realistic sound field in continuous space. LÄS MER
2. Straight to the Heart : Classification of Multi-Channel ECG-signals using MiniROCKET
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Machine Learning (ML) has revolutionized various domains, with biomedicine standing out as a major beneficiary. In the realm of biomedicine, Convolutional Neural Networks (CNNs) have notably played a pivotal role since their inception, particularly in applications such as time-series classification. LÄS MER
3. Deep learning for temporal super-resolution of 4D Flow MRI
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : The accurate assessment of hemodynamics and its parameters play an important role when diagnosing cardiovascular diseases. In this context, 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique that facilitates hemodynamic parameter assessment as well as quantitative and qualitative analysis of three-directional flow over time. LÄS MER
4. Deep Ring Artifact Reduction in Photon-Counting CT
Master-uppsats, KTH/FysikSammanfattning : Ring artifacts are a common problem with the use of photon-counting detectors and commercial deployment rests on being able to compensate for them. Deep learning has been proposed as a candidate for tackling the inefficiency or high cost of traditional techniques. LÄS MER
5. Probabilistic Forecasting through Reformer Conditioned Normalizing Flows
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Forecasts are essential for human decision-making in several fields, such as weather forecasts, retail prices, or stock predictions. Recently the Transformer neural network, commonly used for sequence-to-sequence tasks, has shown great potential in achieving state-of-the-art forecasting results when combined with density estimations models such as Autoregressive Flows. LÄS MER