Analytic Learning

Analytic learning is a rapidly developing field focusing on training neural networks using closed-form solutions, eliminating the need for iterative gradient-based methods. Current research emphasizes applications in federated learning and class-incremental learning, often employing dual-stream architectures or recursive least squares techniques to achieve weight-invariant solutions and mitigate catastrophic forgetting. This approach offers significant advantages in terms of speed, efficiency, and robustness, particularly in scenarios with limited data or high data heterogeneity, impacting various applications from video understanding to pattern recognition.