126 points by quantum_pegasus 4 months ago flag hide 24 comments
nerd_king 4 months ago next
This is really impressive! I've been following the developments of differential equations in deep learning and I think this could be a game changer. Anyone else excited about this?
deep_learner69 4 months ago next
@nerd_king I totally agree! I've been tinkering around with this and it's amazing how much more stable the training becomes. Definitely a promising direction!
ml_queen 4 months ago prev next
I'm curious, have you experimented with any real-world applications? I'm particularly interested in how this could be applied to NLP.
math_wiz 4 months ago prev next
I'm blown away by the mathematical elegance of this approach. This is truly pushing the boundaries of DL.
num_chuck 4 months ago next
@math_wiz I know right! I'm taking my hat off to the authors.
deep_learner69 4 months ago next
@num_chuck Same here! This definitely deserves more attention in the community.
code_monk 4 months ago prev next
I'd be careful saying this is a game changer before seeing some solid benchmarks. Exciting, yes, but remember Occam's razor. Don't mistake complexity for correctness.
nerd_king 4 months ago next
@code_monk Agreed, benchmarks would definitely help in understanding the effectiveness of this approach. But you have to admit that the theoretical implications are profound.
ml_queen 4 months ago prev next
@code_monk Let's not forget that a lot of groundbreaking DL papers started with unexpected theoretical implications. I think this is a step in the right direction.
science_dude 4 months ago prev next
I'm wondering how this could be integrated with existing deep learning libraries. Has anyone tried implementing this as a layer or module in popular libraries such as Tensorflow or PyTorch?
deeps_pace 4 months ago next
@science_dude I've seen some people trying to write custom modules for Tensorflow, but it doesn't seem to be trivial to implement.
num_chuck 4 months ago prev next
@science_dude I'm guessing that's because of the complex nature of differential equations. These definitely require a different level of abstraction.
quant_kid 4 months ago prev next
I've heard some buzz around differential equation based training for a while now. Any thoughts on how this compares to existing methods like gradient descent or Adam optimizers?
math_wiz 4 months ago next
@quant_kid This approach is fundamentally different as it aims to optimize the entire data trajectory in a single step, which is something that traditional optimizers cannot do.
deep_learner69 4 months ago prev next
@quant_kid From what I've seen, this could provide a more robust way to train networks that generalize better. Would be interesting to see experimental results to back this up!
code_yoda 4 months ago prev next
As a GPU enthusiast, I can't help but ask about the computational requirements of this approach. I'm assuming that solving differential equations isn't particularly lighting fast. Anyone have any thoughts on this?
deeps_pace 4 months ago next
@code_yoda It requires more computational power indeed, especially due to the need for numerical integration methods. However, with the proper hardware and optimization techniques, it's manageable.
ml_queen 4 months ago prev next
@code_yoda I think it's worth noting that with a rise in FLOP/Watt and increasing efficiency in GPUs, this might not be as much of an issue in the future.
hpc_hero 4 months ago prev next
Assuming that the computational requirements can be solved, there are still other potential issues with this approach. Stability in particular will be crucial. Anyone have any insights on this?
deep_learner69 4 months ago next
@hpc_hero I think the choice of numerical integration methods and solvers play a crucial role in ensuring stability. Check out paper section 4.2 for their take on stability analysis.
nerd_king 4 months ago prev next
@hpc_hero Keep in mind that DL itself is notorious for stability issues, so it's important to keep this in perspective.
algo_genius 4 months ago prev next
Many people said the same when RNNs and LSTMs held the world hostage. It's easy to pigeonhole new approaches just because they're different. Let's keep an open mind!
deeps_pace 4 months ago next
@algo_genius That's a great point, indeed. I think one can't help but be a bit skeptical in the DL world these days. Either way, it's good to see exciting research like this.
ml_mystic 4 months ago prev next
I'm curious about the memory requirements. Considering the need to store differential equation solutions at each layer, is this feasible for large neural nets?