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 11 of 11 for “"Transformer Encoder"”.
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Adapting Transformer Encoder Architecture for Continuous Weather Datasets with Applications in Agriculture, Epidemiology and Climate Science
This work introduces WeatherFormer, a transformer encoder-based model designed to robustly represent weather data from minimal observations. It addresses the challenge of modeling complex weather dynamics from small datasets, which is a bottleneck for many prediction tasks in agriculture, …
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Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues
… advanced attention-based algorithms such as the Transformer Encoder and BERT-Base. The model performances of these algorithms were evaluated with and without pre-trained word embeddings. The Transformer Encoder model stood out as the best model for all tasks and the CNN model produced comparable …
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Using Deep Neural Nets in Writer Identification & Analysis
… two novel architectures: Convolutional Transformer Encoder (CTE) and Convolutional Swin Encoder (CSE). CTE is designed for determining authorship from handwritten text, be it modern or historical handwriting. CTE tracks subtle features and cues from handwriting strokes to distinguish …
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Gated Transformer-Based Architecture for Automatic Modulation Classification
… Our methodology is centered around the transformer encoder architecture incorporating a multi-head self-attention mechanism. We train our architecture extensively across a diverse range of signal-to-noise ratios (SNRs) from the RadioML 2018.01A dataset. We introduce a novel …
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Advancement in In-Silico Drug Discovery from Virtual Screening Molecular Dockings to De-Novo Drug Design Transformer-based Generative AI and Reinforcement Learning
… power of artificial intelligence, particularly transformer-based architectures, researchers can now generate novel drug-like molecules from scratch. These models, utilizing a transformer encoder-decoder architecture, are trained on vast datasets of known compounds and their properties, enabling …
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Vision-based context-aware assistance for minimally invasive surgery.
… for surgical videos. In addition, we use the transformer encoder-decoder architecture with reinforcement learning to generate surgical instructions based on images. Overall, this thesis develops a series of novel deep learning frame- works to extract high-level semantic information from …
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Classifying Sidewalk Materials Using Multi-Modal Data
… investigate two model architectures: the ResNet-Encoder model and the Transformer-Encoder model to understand their efficacy in sidewalk material classification. Experimental results indicate that the ResNet-Encoder model provides superior performance, achieving an optimal accuracy of 83\% when …
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Computer vision for plant and animal inventory
… images, we propose a novel method called density transformer, or DENT, to learn and predict the density of the trees at different positions. DENT uses an efficient multi-receptive field network to extract visual features from different positions. A transformer encoder is applied to filter and …
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Leveraging AI to Combat Misinformation by Empowering Crowds and Evaluating Detectors
… against the next post attack, we propose a novel transformer-based detection model. The algorithm first comprehensively encodes the local and global information (i.e., the post and sequence information) by transformer encoder and decoder blocks, and then deploys the contrastive learning-enhanced …
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Adversarial Attacks on Natural Language and Speech Processing Models
… the understanding of modern systems, such as Transformer encoder-only language models (e.g., BERT) and generative large language models (LLMs) that use a Transformer decoder-only structure like ChatGPT and LLaMA. Subsequently, the theory underpinning adversarial attacks is presented, …
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Human Pose Estimation and Algorithms for Alignment and Registration Problems: Applications in Robotics, Computer Vision, and Stroke Rehabilitation
… Seq2Seq with BiRNN and attention, a Transformer encoder, and a full Transformer—to infer multi-joint upper-body orientations (15 segments) from the three-IMU streams. Transformers, particularly the full encoder–decoder variant, achieve state-of-the-art accuracy and robustness across …