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.
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Showing 1 to 20 of 1100 for “"convolutional"”.
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Convolutional Conditional Neural Processes
… processes in three ways. First, we propose convolutional neural processes (ConvNPs). ConvNPs improve data efficiency of neural processes by building in a symmetry called translation equivariance. ConvNPs rely on convolutional neural networks rather than multi-layer perceptrons. Second, we …
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Counting with convolutional neural networks
In this work, we tackle the question: Can neural networks count? More precisely, given an input image with a certain number of objects, can a neural network tell how many are there? To study this, we create a synthetic dataset consisting of black and white images with variable numbers of white …
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Error mechanisms for convolutional codes.
Massachusetts Institute of Technology. Dept. of Electrical Engineering. Thesis. 1968. Ph.D.
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Visualization of Deep Convolutional Neural Networks
… scene recognition tasks, especially after the Convolutional Neural Network (CNN) model was introduced. Although a CNN often demonstrates very good classification results, it is usually unclear how or why a classification result is achieved. The objective of this thesis is to explore several …
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Stable and symmetric convolutional neural network
DSpace SAF Submission Ingestion Package generated from Vireo submission #9397 on 2016-11-09 at 10:19:06
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ConvMLP: Hierarchical convolutional MLPS for vision
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Dynamic Spatio-Temporal Graph Convolutional Networks
… with changing graph structure. DST-GCN employs a convolutional architecture to learn spatio-temporal relationships that provide strong generalization and attractive computational efficiency. We provide empirical results for several datasets from different domains that demonstrate the gains …
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Protein Function Prediction Using Graph Convolutional Network
… protein language models (PLMs) and graph convolutional networks (GCNs), addressing the limitations of traditional methods that rely heavily on sequence similarity. The proposed model leverages diverse protein features, including sequences, protein-protein interaction (PPI) networks, and …
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Classifying GPR images using convolutional neural networks
… medium using four different architectures of convolutional neural networks. Two CNNs were newly proposed for this study, while the other two were used by other authors. These CNNs were trained using a couple of adjusted training options including initial learning rate, learn rate drop factor, …
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Galaxy classification with deep convolutional neural networks
… images are not suitable for galaxy images. Deep convolutional neural networks (CNNs) are able to learn powerful features from images by hierarchical convolutional and pooling operations. This work applies state-of-the-art deep CNN technologies to galaxy classification for both a regression task …
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Efficient convolutional neural network inference on microcontrollers
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01
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Greedy layerwise training of convolutional neural networks
… to end-to-end back-propagation for training deep convolutional neural networks. Although previous work was unsuccessful in demonstrating the viability of layerwise training, especially on large-scale datasets such as ImageNet, recent work has shown that layerwise training on specific architectures …
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Asymptotically good convolutional codes with feedback encoders
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Handling Invalid Pixels in Convolutional Neural Networks
Most neural networks use a normal convolutional layer that assumes that all input pixels are valid pixels. However, pixels added to the input through padding result in adding extra information that was not initially present. This extra information can be considered invalid. Invalid pixels can also …
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Joint spatial and layer attention for convolutional networks
… that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., “what” feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., “where”) to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) …
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Classification of Variable Stars using Convolutional Neural Network
<p>This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and …
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Electricity Price Forecasting Using a Convolutional Neural Network
… the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional neural networks are used for this time series forecasting …
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Learning from videos with deep convolutional LSTM networks
… in videos. A deep network of convolutional LSTMs allows the model to access the entire range of temporal information at all spatial scales of the data. This work first constructs an MNIST-based video dataset with parameters controlling relevant facets of common video-related …
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