Browsing: Computational Complexity

Transformer-based Large Language Models (LLMs) have emerged as the backbone of Natural Language Processing (NLP) due to their creative self-attention mechanism. However, self-attention layers…

Audio classification has evolved with the adoption of deep learning models, particularly transformer-based architectures. These models offer improved performance and the ability to handle…

This article discusses the challenge of extracting information from noisy environments in both classical and quantum computing. The authors propose a quantum smoothing filter…

Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a more efficient computer vision model that vastly reduces the computational complexity…

Recent research has demonstrated that shallow feedforward networks can learn non-trivial classification tasks with reduced computational complexity compared to deep learning architectures. This discovery…

This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains…