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 41 for “"attention mechanisms"”.
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Graph Attention Mechanisms for Modeling Pathway-Level Importance from Gene Expression
… a comprehensive understanding of the complex mechanisms underlying molecular biology. However, the high dimensionality and noise inherent in gene expression data pose significant challenges for biomarker discovery and molecular subtype classification in cancer. While deep learning models have …
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Revolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms
… by incorporating a cycling layer into self-attention mechanisms. Traditional techniques often struggle to capture the cyclical nature of time series data, impacting predictive model accuracy. By integrating a cycling layer, this thesis aims to enhance the ability of models to recognize and …
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Development of Novel Attention-Aware Deep Learning Models and Their Applications in Computer Vision and Dynamical System Calibration
… such as healthcare. To address this, attention-based methods have been proposed to enhance the focus and interpretability of deep learning models. In this dissertation, we investigate the effectiveness of attention mechanisms in improving prediction and modeling tasks across different …
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Towards Explainable Event Detection and Extraction
… advances in representation learning by using attention mechanisms. Specifically, for event detection we propose a method to compute document embeddings from sentence embeddings by leveraging attention and training a document classifier on those embeddings to mitigate the error propagation …
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Neural attentions for natural language understanding and modeling
In this thesis, we explore the use of neural attention mechanisms for improving natural language representation learning, a fundamental concept for modern natural language processing. With the proposed attention algorithms, our model made significant improvements in both language modeling and …
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Analysis of signal resampling effects on attention-driven SEI for IoT systems
… IoT devices. This work evaluates an attention-based SEI model that maintains high identification accuracy at reduced sampling rates. The proposed approach achieves over 97% accuracy using only 2,500 signals sampled at 5 MHz and sustains above 90% accuracy under Rayleigh fading, …
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An attention-enhanced student–teacher framework for structural and logical anomaly detection in industrial settings
… anomaly detection scheme (c. 2024) with self-attention mechanisms, enabling more effective relational modeling. We demonstrate consistent improvements on the MVTec LOCO Anomaly Detection benchmark. Specifically, AeCSAD employs a global student network with self-attention for reasoning across …
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Robust graph representation learning with structure-aware attention and self-supervised contrastive frameworks.
… tasks. Specifically, I investigate attention-based Graph Neural Networks (GNNs) and their variants, designing structure-aware and contrastive learning strategies to capture both local and global dependencies in graphs. Through extensive experiments on benchmark and real-world …
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Commonsense for Zero-Shot Natural Language Video Localization
… graph, conditioned on the video, and cross-attention mechanisms to enhance the encoded video and pseudo-query vectors prior to localization. Through empirical evaluations on two benchmark datasets, we demonstrate that our model surpasses both zero-shot and weakly supervised baselines. These …
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The Structural Constraint and The Strategic Control of Attention Allocation
… time-sharing efficiency (joint performance) and attention allocation optimality (consistency of protecting the performance of the high priority task). Time-sharing performance was evaluated as: (a) structural configurations of the time-shared tasks, (b) dynamics of the difficulty changes, and (c) …
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Advancements in Word Alignment: Introducing a Novel Count-Based Subword Model Alongside Neural and Ensemble Models
… employs an encoder-decoder architecture with attention mechanisms for end-to-end alignment learning. The ensemble model combines the strengths of both the count-based and neural models to improve alignment accuracy and robustness. Through extensive experimentation, we demonstrate the …
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Modeling with Attention in Demand Forecasting and Beyond
… medical products follow. We also propose new attention mechanisms attending over features and time series independently to generate more descriptive interactions. Ultimately we hope to combine this Transformer with more traditional time series models such as Holt-Winters as a way to alleviate …
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Efficient methods for mapping neural machine translator on FPGAs
… as bidirectional gated recurrent units (GRU), attention mechanisms, and beam-search algorithms, for improved translation quality. However, with the increasing problem size, the real-life NMT models have become much more complicated and difficult to implement on hardware for acceleration …
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The role of attention in spatial (dis)orientation of older adults.
… to enhance our understanding of the cognitive mechanisms which contribute to declining navigation ability, with a particular emphasis on attentional processes. Two place recognition experiments confirmed age-related deficits in overall place learning ability. These deficits were particularly …
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Attention mechanism in deep neural networks for computer vision tasks
<p>“Attention mechanism, which is one of the most important algorithms in the deep Learning community, was initially designed in the natural language processing for enhancing the feature representation of key sentence fragments over the context. In recent years, the attention mechanism has been …
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Development of a Fully Automated Al System for Skeletal Maturity Assessment Using Cone Beam CT
… ResNet, EffNet, and ConvNeXt (with and without attention mechanisms). In addition, several adjunctive models and techniques—including YOLO, SAM, Grad-CAM, knowledge distillation, and demographic conditioning—were incorporated into the development of several frameworks. The highest-performing, …
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Long-Term Memory Biases Auditory Spatial Attention: Healthy and Impaired “Memory-Guided Attention”
Attention and memory may be impaired in individuals at-risk for Alzheimer’s disease (AD), though standard cognitive assessments typically study the two in parallel. In reality, attention and memory interact to facilitate processing, and thus a more integrative approach is required. Here, I used a …
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Graph Neural Networks for Health-Aware Food and Multi-Criteria Recommendation Systems
… Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA) and the Multiview Graph Dual Attention and Contrastive Learning for Multi-Criteria Recommender Systems (D-MGAC). The first framework, HFRS-DA, addresses the challenge of effectively integrating …
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REPRESENTATION LEARNING FOR VISUAL TASKS: A STUDY OF ATTENTION AND INFORMATION SELECTION
… visual representation learning by optimizing attention mechanisms and information selection strategies within deep learning models. Standard approaches often process images independently and compress them into single global descriptors, limiting performance on tasks requiring contextual …
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Object Detection Using Vision Transformed EfficientDet
… natural language processing tasks, employs self-attention mechanisms to capture global dependencies in input images. However, ViT’s capability to capture fine-grained details and context information is limited. To address this limitation, the integration of ViT into the EfficientDet architecture …
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