Sökning: "Branched Neural Network"

Hittade 3 uppsatser innehållade orden Branched Neural Network.

1. 1. Deep and Machine Learning on Imaging Flow Cytometry

   Master-uppsats, Uppsala universitet/Institutionen för farmaceutisk biovetenskap

   Författare :Xinyi Dai; [2022]
   Nyckelord :Imaging flow cytometry; image-based analysis; machine learning;

   Sammanfattning : Cell painting uses fluorescent agents to label the compositions or organelles of cells to evoke morphological profiling. Imaging flow cytometry (IFC) is a multi-channel imaging technique to acquire individual cell images, including the brightfield and multiple single fluorescence channels. LÄS MER

3. 2. Multitask Convolutional Neural Network Emulators for Global Crop Models - Supervised Deep Learning in Large Hypercubes of Non-IID Data

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Amanda Nilsson; [2020]
   Nyckelord :Multitask Learning; Convolutional Neural Network CNN ; Branched Neural Network; Dynamic Global Vegetation Models DGVM ; Automated Feature Extraction; Feature Importance; Supervised Machine Learning; Emulator; Surrogate Model; Response Surface Model; Approximation Model; Metamodeling; Model Composition; Regularization; Robustness; Hyperparameter Optimization; Mathematics and Statistics;

   Sammanfattning : The aim of this thesis is to establish whether a neural network (NN) can be used for emulation of simulated global crop production - retrieved from the computationally demanding dynamic global vegetation model (DGVM) Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS). It has been devoted to elaboration with various types of neural network architectures: Branched NNs capable of processing inputs of mixed data types; Convolutional Neural Network (CNN) architectures able to perform automated temporal feature extraction of the given weather time series; simpler fully connected (FC) structures as well as Multitask NNs. LÄS MER

4. 3. Peptide Retention Time Prediction using Artificial Neural Networks

   Master-uppsats, KTH/Matematisk statistik

   Författare :Sara Väljamets; [2016]
   Nyckelord :;

   Sammanfattning : This thesis describes the development and evaluation of an artificial neural network, trained to predict the chromatographic retention times of peptides, based on their amino acid sequence. The purpose of accurately predicting retention times is to increase the number of protein identifications in shotgun proteomics and to improve targeted mass spectrometry experiment. LÄS MER