Abstract
Background: In the modern academic jungle full of noise, overfitting and gradient explosion, maintaining a long-term and stable intimate relationship is a non-convex optimization problem that is extremely difficult to converge due to too many parameters.
Method: This article proposes a robust framework based on "two-way redemption". We introduced a pre-trained model named "Robust Agent" and a latent variable named "Active Learner". In the experiment, we successfully resisted the external disturbances from "Tutor PUA", "Industry Winter" and "FPU Error" by introducing "Apple Grilled Chicken", "Beef Rib Grill" and "Salmon" as regularization terms.
Result: Experiments show that although the Loss value (emotional fluctuation) oscillates many times during the training process, and even risks NaN (collapse), as long as the two models maintain high-frequency parameter synchronization, they will eventually converge to a global optimal solution called "love".
Conclusion: Love is not a straight line, love fluctuates. But as long as the mean of the fluctuation is you, no matter how large the variance is, I can accept it.
Concept: Urban campus love story