Transformer Based Neural Network

Transformer-based neural networks are a powerful class of deep learning models achieving state-of-the-art results across diverse applications, from natural language processing and computer vision to time series forecasting and scientific data analysis. Current research focuses on adapting transformer architectures for specific data types (e.g., graphs, spiking neural networks, tabular data) and improving efficiency and robustness through techniques like wavelet transformations, regularized attention, and novel training algorithms. This ongoing work significantly impacts various fields by enabling more accurate and efficient solutions to complex problems, particularly in areas requiring the processing of long-range dependencies and intricate relationships within data.