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 20 of 29 for “"LSTMs"”.
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Utilizing GAN and Sequence Based LSTMs on Post-RF Metadata for Near Real Time Analysis
Wireless anomaly detection is a mature field with several unique solutions. This thesis aims to describe a novel way of detecting wireless anomalies using metadata analysis based methods. The metadata is processed and analyzed by a LSTM based Autoencoder and a LSTM based feature analyzer to produce …
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Biological applications, visualizations, and extensions of the long short-term memory network
… efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus on biological tasks pertaining to physiological signals, peripheral neural signals, and molecules. This goal drives the three subplots in this …
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Learning from videos with deep convolutional LSTM networks
… This research explores the use of convolution LSTMs to simultaneously learn spatial- and temporal-information 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 …
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Assessing the Robustness of Deep Learning Streamflow Models Under Climate Change
… (the NOAA National WaterModel), and found that LSTMs were generally more accurate than both, even when trained on climatologically biased data splits. The process-based model did not show as large of a performance gap as the conceptual and deep learning models, however (i) this model was not …
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Evaluating transformers as memory systems in reinforcement learning
… of the Transformer architecture may outperform LSTMs as memory networks in reinforcement learning. However, our results indicate that while gated variants of the Transformer architecture may be able to model dependencies over a longer temporal horizon, these models do not necessarily outperform …
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Latent Compositional Representations for English Function Word Comprehension
… tree-compositional models perform as well as LSTMs, but lag behind BERT to varying degrees between tasks. Context-dependent behavior of tree-compositional models highlights a potential weakness of the architecture in the absence of grounding information.
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Learning sentiment and semantic relatedness in user generated content using neural models
… (CNNs) and Long Short-Term Memory Networks (LSTMs) to capture the semantic and sentiment information. Aspect Based Sentiment Analysis is concerned with predicting the aspect categories mentioned in a sentence and the sentiments associated with each aspect category. We refer to these tasks as …
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Machine learning for time series anomaly detection
… I used the "Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding" paper as a basis for my analysis, and was able to beat their performance on anomaly detection by obtaining an F0.5 score of 76%, an improvement over their 69% score.
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Finding similar questions in large-scale community QA forums
… neural network architectures (including CNNs and LSTMs) trained analogously.
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Higher Order Recurrent Neural Network for Language Modeling
… the regular RNNs as well as the popular LSTMs.
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Physical symmetry enhanced neural networks
… neural networks like long short-term memory (LSTMs) and gated recurrent units (GRUs) in many sequential tasks. In the third part, we develop a convolutional neural network architecture that achieves logarithmic scale complexity using symmetry breaking concepts. We demonstrate that our model …
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Link Prediction on Distributed Systems
… predictive models, including Random Walk, GNNs, LSTMs, and Transformer Models, and explores the impact of time windowing strategies, dataset variations, and scalability. Additionally, the research investigates the feasibility of real-time predictions and the identification of recurring patterns …
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Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction
… decisions. While deep learning models such as LSTMs have demonstrated strong predictive performance on multivariate clinical time series, they often lack interpretability. To address this gap, this thesis proposes a framework that combines the predictive strength of neural networks with the …
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Reliable control of surgical robots and stabilization of long short-term memory neural networks
… of Long Short-Term Memory Neural Networks (LSTMs). Such studies are needed in order have better insights into how and why LSTM models work so well with time-series data, with the ultimate goal of improving their training and performance. Towards that direction, two sufficient conditions on …
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A Study of Machine Learning based on Physical Load Classification
… rate. The excellent performance of multiple Bi-LSTMs for binary classification problems was exploited by changing the discriminative approach of the network, and a correct recognition rate of 94.1% was achieved for 3 classifications using 3 binary Bi-LSTM networks. This method can be used to …
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Interpretable neural models for natural language processing
… compared to alternative architectures (such as LSTMs and CNNs) across several NLP applications. In the second part, we learn rationales behind the model's prediction by extracting input pieces as supporting evidence. Rationales are tailored to be short and coherent, yet sufficient for making the …
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Improving Data-Driven Contact Localization and Force Estimation for Barometric Tactile Sensors
… I find that more expressive models such as LSTMs tend to overfit on the collected data and physical phenomena observed during deployment were not well represented by the training metrics. To better understand the extent that these data-driven methods alone can improve sensor performance, I …
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Neural attentions for natural language understanding and modeling
… to simulate long short-term memory networks (LSTMs). The model process sequential data in parallel and still achieves competitive performances. We also propose a phrase induction model and headword attention to learn the embedding of following phrases. The model is able to learn reasonable …
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Predicting diarrhoea outbreak with climate change
… (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to …
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Challenges of computational social science analysis with NLP methods
… architectures ranging from unidirectional LSTMs to Transformer based architectures. In the second part of the thesis, we shift our focus to fairness, a central concern in CSS. Our goal in this part of the thesis is to analyze and improve the performances of NLP models used in CSS in terms …
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