This thesis investigates the implementation of image denoising using Neural Networks (NN) in conjunction with wavelet transform techniques. For the purpose of data simulation, images are intentionally corrupted with additive white Gaussian noise (AWGN) at vari…
Mixture-of-Experts (MoE) architectures decouple representational capacity from per-token computational cost: each input activates only a few specialised experts, yielding far greater capacity than dense models at equal activation cost. Federated Learning (FL)…
Medical Image Semantic Segmentation (MISS), the process of assigning a semantic label to each pixel in an image, is a foundational task in computational medicine, critical for quantitative diagnostics and treatment planning. However, developing robust MISS mod…
In this thesis, quantization and knowledge distillation are applied to a transformer model with the inhibitor attention mechanism and the results are compared to that of a scaled dot-product attention (SDPA) model. The two main questions that are explored in t…
Transformer-based models have revolutionized natural language processing while becoming increasingly opaque in their decision-making processes. As these systems deploy in high-stakes domains such as healthcare, legal decision-making, and content moderation, th…
Artificial Neural Networks (ANNs) are among the leading computational frameworks for approximating human visual processing across a range of visual tasks. Despite this, a notable gap remains between human and ANN performance in tasks that require social percep…
Modern datasets are increasingly high-dimensional and multiway, naturally represented as tensors with multiple indexed variables. Such data arise in numerous applications, including multivariate time series forecasting, 3D medical imaging, and climate modeling…
[EN] This research explores the application of Transformer-based models and Hybrid Attention Mechanisms across three critical biomedical tasks: meningioma segmentation, epileptic seizure detection, and pathogenicity prediction of genomic variants. For meningio…
This master’s thesis explores the trade-off between computational efficiency and modeling performance in Transformer architectures by studying and improving attention mechanisms. The work consists of two parts: (1) a comparative empirical study evaluating five…
In recent years, the Transformer achieved remarkable results in computer vision related tasks, matching, or even surpassing those of convolutional neural networks (CNN). However, unlike CNNs, those vision transformers lack strong inductive biases and, to achie…
Transformer models have been quickly taking over many tasks in the field of deep learning, due to their versatility while maintaining high performance. This thesis provides an introduction to transformer models, presents experiments with new ways of sampling d…
Deep learning (DL) algorithms have produced superior results for a variety of vision, language, and speech processing tasks, but have proven to be power and compute-hungry. In the past, hardware advancements have met the growing need for compute. Yet in recent…
أنا مساعد المستودع. اسألني عن الرسائل والأطاريح حسب الموضوع أو الجامعة أو الدولة أو السنة أو الدرجة، أو اطلب أعداداً وإحصاءات.
يجيب من قاعدة بيانات المستودع فقط، ولا يخترع سجلات أو أرقاماً. لا يُحفظ ما تكتبه على الخادم.
قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
