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 6 of 6 for “"sequence modelling"”.
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Sequence modelling using deep learning approaches for spatiotemporal public transport data.
Encouraging the use of public transport is essential to combat congestion and pollution in an urban environment. To achieve this, the reliability of public transport arrival time prediction should be improved, as this is often requested by passengers. This will make the use of urban bus networks …
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INVERSE APPROXIMATION THEORY OF RECURRENT MODELS FOR LEARNING SEQUENCES
… long-term relationships is a challenging task in sequence modelling. Despite numerous empirical results demonstrating the difficulty of recurrent models in learning long-term relationships, this dissertation presents a series of theoretical studies on the learning of long-term memories using …
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Higher Order Recurrent Neural Network for Language Modeling
… mechanism to learn long term dependency in sequences. Analogous to digital filters in signal processing, we call these structures as higher order RNNs (HORNNs). Similar to RNNs, HORNNs can also be learned using the back-propagation through time method. HORNNs are generally applicable to a …
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Ensemble generation and compression for speech recognition
… that the ensemble can have. Second, ASR is a sequence modelling task, and the frame-level posteriors that are propagated may not effectively convey all information about the sequence-level behaviours of the teachers. This thesis addresses both of these limitations. The second contribution of …
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Towards Energy-Efficient Cloud Datacentres: A Unified Framework for Generative and Multi-Scale Time Series Forecasting
… adversarial learning with attention-based sequence modelling to enhance long-horizon prediction accuracy. To further improve robustness and generalisability under volatile conditions, DAA-T-GAN is proposed, integrating diffusion-inspired data augmentation and transformative adversarial …
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Log-Driven Robust Anomaly Detection and Root Cause Localisation for Distributed Computing Systems
… policy with a two layer LSTM over event sequences. The detector is trained on representative successful runs and assigns each session an anomaly score that supports threshold based triage. Experiments on an OpenStack corpus collected in our laboratory show clear separation between normal …