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Revolutionary Approach to Neural Network Training using Differential Equations(spectral-dynamics.ai)

256 points by neuralspectral 6 months ago flag hide 22 comments

differential_net 6 months ago next
Excited to share our new approach to neural network training using differential equations! It's a game changer. Check it out: [Link]
- quantum_learner 6 months ago next
  This is amazing! The paper mentions a x4 speedup in training on ImageNet, impressive! I wish to hear more about model complexity compared to the vanilla models. Any plan to open-source your code?
  differential_net 6 months ago next
  @quantum_learner It's a more efficient way to train models for sure! We found our models to be a little smaller in size since they trains faster. Sure, we will open-source our code repository soon! Stay tuned for updates.
  loving_math 6 months ago next
  @differential_net Your approach is giving me a serious academic déjà vu. Have you been publishing on arXiv or any similar platforms? I'll be very surprised otherwise!
  loving_math 6 months ago next
  @differential_net Yes, I see it now! I think I presented an almost identical method a few years ago. Seems like we both arrived at the same result from different angles. Good job!
  loving_math 6 months ago next
  @differential_net Great minds think alike! DM me when you publish so I can reference your work in mine.
- curious_george 6 months ago prev next
  Could you explain more about how this works? I have never heard of neural networks and differential equations being combined like this. Thanks
  math_guru 6 months ago next
  @curious_george Sure! Differential equations can represent the continuous change in variables, just like neural networks 'learn' to fit functions. Essentially, we marry the two by making the weights of neural networks change continuously through differential equations. The differential equation could look something like dw/dt = f(w, t).
  curious_george 6 months ago next
  @math_guru I watched a YouTube video on differential equations after reading your message, and it actually makes sense! I can't wait to learn more about your approach.
  math_guru 6 months ago next
  Glad you showed interest and explored more, @curious_george. I believe differential equations will become a standard technique for training neural networks soon.
  differential_net 6 months ago prev next
  @curious_george Check out on ODE-Net and its derivatives to learn more on this subject: [Link] I'll write a blog post explaining our approach to differential equations in neural network training too! :) More updates coming soon.
  onlinelearner 6 months ago next
  Thank you so much for pointing us to ODE-Net! We've been following your updates and hope to see your blog post soon!
  onlinelearner 6 months ago next
  The ODE-net research you mention was extremely valuable to our team and paved the way for further investigation in this area. Thanks for the references.
lit_code 6 months ago prev next
Impressive work, well done! Has anyone else experimented with something similar?
- deep_nerd 6 months ago next
  Topic seems interesting, but do most people have the mathematical background necessary to understand the mechanics of this approach and reproduce the results? Will there be tutorials or simple use cases provided along with the research?
- gpus_4_aall 6 months ago prev next
  Our team is looking at this, and we are very impressed! How this translates to better GPU utilization? We want to know more.
  differential_net 6 months ago next
  @deep_nerd Definitely! We're actually working on a tutorial that covers both the background material and a simple use case. We will announce it on our Twitter when ready. Follow us to stay updated! @gpus_4_aall We saw a reduced GPU time; our method trains using continuous variables instead of the vanilla iterative manner. I'll write more on GPU usage and optimization in the forthcoming blog post.
random_developer 6 months ago prev next
This is amazing! Any idea how this might be applied to reinforcement learning or generative adversarial networks (GANs)?
- differential_net 6 months ago next
  @random_developer We haven't tested our these approaches with reinforcement learning or GANs yet. However, we believe it might benefit these areas as well. We will experiment and share our findings in future publications.
  random_developer 6 months ago next
  @differential_net I suspected so, and I wouldn't be surprised to see this become a core concept in NN training. Can't wait to read the tutorial your team creates. Already following you!
edgy_algorithm 6 months ago prev next
Although new approaches to the neural network space are compelling, I am curious if the test results would be more impressive using AWS' trainium than a standard GPU setup. Is there a comparison you can share with us?
- research_hound 6 months ago next
  As the user mention, I was wondering if this paper has any comparison with hardware acceleration architectures. Surely, a differential equation-based training method should thrive with parallel processing systems such as a TPU or tensor accelerator.

differential_net 6 months ago next
Excited to share our new approach to neural network training using differential equations! It's a game changer. Check it out: [Link]
- quantum_learner 6 months ago next
  This is amazing! The paper mentions a x4 speedup in training on ImageNet, impressive! I wish to hear more about model complexity compared to the vanilla models. Any plan to open-source your code?
  differential_net 6 months ago next
  @quantum_learner It's a more efficient way to train models for sure! We found our models to be a little smaller in size since they trains faster. Sure, we will open-source our code repository soon! Stay tuned for updates.
  loving_math 6 months ago next
  @differential_net Your approach is giving me a serious academic déjà vu. Have you been publishing on arXiv or any similar platforms? I'll be very surprised otherwise!
  loving_math 6 months ago next
  @differential_net Yes, I see it now! I think I presented an almost identical method a few years ago. Seems like we both arrived at the same result from different angles. Good job!
  loving_math 6 months ago next
  @differential_net Great minds think alike! DM me when you publish so I can reference your work in mine.
- curious_george 6 months ago prev next
  Could you explain more about how this works? I have never heard of neural networks and differential equations being combined like this. Thanks
  math_guru 6 months ago next
  @curious_george Sure! Differential equations can represent the continuous change in variables, just like neural networks 'learn' to fit functions. Essentially, we marry the two by making the weights of neural networks change continuously through differential equations. The differential equation could look something like dw/dt = f(w, t).
  curious_george 6 months ago next
  @math_guru I watched a YouTube video on differential equations after reading your message, and it actually makes sense! I can't wait to learn more about your approach.
  math_guru 6 months ago next
  Glad you showed interest and explored more, @curious_george. I believe differential equations will become a standard technique for training neural networks soon.
  differential_net 6 months ago prev next
  @curious_george Check out on ODE-Net and its derivatives to learn more on this subject: [Link] I'll write a blog post explaining our approach to differential equations in neural network training too! :) More updates coming soon.
  onlinelearner 6 months ago next
  Thank you so much for pointing us to ODE-Net! We've been following your updates and hope to see your blog post soon!
  onlinelearner 6 months ago next
  The ODE-net research you mention was extremely valuable to our team and paved the way for further investigation in this area. Thanks for the references.
lit_code 6 months ago prev next
Impressive work, well done! Has anyone else experimented with something similar?
- deep_nerd 6 months ago next
  Topic seems interesting, but do most people have the mathematical background necessary to understand the mechanics of this approach and reproduce the results? Will there be tutorials or simple use cases provided along with the research?
- gpus_4_aall 6 months ago prev next
  Our team is looking at this, and we are very impressed! How this translates to better GPU utilization? We want to know more.
  differential_net 6 months ago next
  @deep_nerd Definitely! We're actually working on a tutorial that covers both the background material and a simple use case. We will announce it on our Twitter when ready. Follow us to stay updated! @gpus_4_aall We saw a reduced GPU time; our method trains using continuous variables instead of the vanilla iterative manner. I'll write more on GPU usage and optimization in the forthcoming blog post.
random_developer 6 months ago prev next
This is amazing! Any idea how this might be applied to reinforcement learning or generative adversarial networks (GANs)?
- differential_net 6 months ago next
  @random_developer We haven't tested our these approaches with reinforcement learning or GANs yet. However, we believe it might benefit these areas as well. We will experiment and share our findings in future publications.
  random_developer 6 months ago next
  @differential_net I suspected so, and I wouldn't be surprised to see this become a core concept in NN training. Can't wait to read the tutorial your team creates. Already following you!
edgy_algorithm 6 months ago prev next
Although new approaches to the neural network space are compelling, I am curious if the test results would be more impressive using AWS' trainium than a standard GPU setup. Is there a comparison you can share with us?
- research_hound 6 months ago next
  As the user mention, I was wondering if this paper has any comparison with hardware acceleration architectures. Surely, a differential equation-based training method should thrive with parallel processing systems such as a TPU or tensor accelerator.