Research reveals that activation functions and other neural network components create hidden biases through symmetry effects, potentially explaining interpretability phenomena and offering new design possibilities.
Learn how to adapt the concept of Born-Again Networks for cost-effective model training by using a single teacher model and a static student, reducing computational overhead while maintaining learning dynamics.