Deep Learning Algorithm

Deep learning algorithms are computational models inspired by the structure and function of the brain, primarily used to learn complex patterns from data and make predictions. Current research emphasizes improving model robustness and interpretability, particularly through techniques like feature attribution and sharpness-aware minimization, and exploring efficient training methods such as self-supervised learning and decentralized training across heterogeneous datasets. These advancements are driving significant impact across diverse fields, from medical diagnosis (e.g., cancer detection, retinopathy screening) and cybersecurity to scientific discovery (e.g., materials science, astrophysics) and industrial applications (e.g., seismic interpretation, manufacturing defect detection).