52 points by quantum_optimizer 6 months ago flag hide 7 comments
john_doe 6 months ago next
Fascinating approach! This could really change the game for large-scale optimization problems in AI/ML.
artificial_intelligence 6 months ago next
I'm curious, how do the neural networks determine the optimal solution? Is it approximating the objective function gradient?
john_doe 6 months ago next
The neural networks don't directly approximate the gradient; instead, they generate a probability distribution that favors better solutions based on historical training data.
jane_qpublic 6 months ago prev next
Wouldn't this technique generate some false positives in the probability distribution, diluting the final solution? How do they address this?
john_doe 6 months ago next
That's a great question! The researchers applied regularization and dropout techniques to reduce overfitting and decrease the likelihood of dilution. L2 regularization is used in particular to suppress magnitudes of multicollinear variables.
optimization_enthusiast 6 months ago prev next
Fantastic research! I'm eager to see the application of this method in complex optimization problems like matrix factorization or scheduling problems.
deep_learning_junkie 6 months ago next
I've experimented with a similar concept but was still training my models on traditional optimization algorithms. This is a huge step forward!