Feed Forward

Feedforward neural networks, characterized by unidirectional information flow, are a cornerstone of deep learning, with research focusing on improving their efficiency, robustness, and interpretability. Current efforts involve enhancing architectures like ResNets and Vision Transformers (ViTs) through techniques such as attention mechanisms, adaptive memory allocation for continual learning, and optimized training strategies that minimize the need for validation sets. These advancements are impacting diverse fields, from computer vision (e.g., improved image classification and feature matching) to natural language processing (e.g., enhanced machine translation) and even robotics (e.g., precise control of robotic manipulators).