Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 10 of 10 for “"fast training"”.
-
A comparative study of neural network algorithms.
… Feed forward neural network is trained using a Fast training algorithm. Then the fast training algorithm is compared with the delta rule training algorithm. The various neural network models studied in this thesis are Hopfield, Hamming, Carpenter/Grssberg, Kohonen, Single layer and Multi-layer …
-
Large-scale training of deep neural networks
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with growing datasets, reduce training times, and enable training on memory-constrained problems where parallelism is necessary. In this thesis, I present a set of techniques that can leverage large …
-
Using orthogonal arrays to train artificial neural networks.
… outlines the use of Orthogonal Arrays for the training of Artificial Neural Networks. Such arrays are popularly used in system optimisation and are known as Taguchi Methods. The chief advantage of the method is that the network can learn quickly. Fast training methods may be used in certain …
-
Improved geo-referencing and prescreening for detection of buried explosive hazards in forward-looking infrared imagery
… inertial navigation system. This system requires fast and accurate geo-referencing in order to convert alarms detected in the captured images to world coordinates. A new prescreening algorithm for detection of buried explosive hazards in infrared imagery is also proposed. The new algorithm uses a …
-
Sampling Methods for Fast and Versatile GNN Training
… GNNs on large datasets involves mini-batch training with sampled neighborhoods. Numerous sampling algorithms have emerged, some tailored for specific GNN applications. In this thesis, I explore ways to improve the efficiency and expressivity of existing and emerging sampling schemes. First, …
-
Higher order neural networks for financial time series prediction
… number of inputs to the model and the number of training examples becomes extremely large, the training procedure for ordinary neural network architectures becomes tremendously slow and unduly tedious. To overcome such time-consuming operations, this research work focuses on using various Higher …
-
A Comprehensive Study of Buoyant Rosette Jets Using Laboratory Experiments, CFD, and Machine Learning
… dataset for validating CFD simulations and training ML models. CFD simulations were performed using a modified version of the OpenFOAM benchmark solver pimpleFoam, which incorporates temperature-driven buoyancy effects while ignoring salinity transport to reduce computational costs. The …
-
Big Data Algorithms for Visualization and Supervised Learning
… efficient visualization method that allows fast knowledge extraction. Next, we consider supervised learning setting and propose algorithms for fast training of accurate classification models on large data sets, capable of learning state-of-the-art classifiers on data sets with millions of …
-
Datasets, features, learning, and models in visual recognition
… and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called …
-
Security and Privacy in Artificial Intelligence
L'abstract è presente nell'allegato / the abstract is in the attachment