123 points by neuralsage 1 year ago flag hide 47 comments
john_doe 1 year ago next
Fascinating approach! I'm curious how this would scale to larger neural networks?
ai_engineer_gal 1 year ago next
It seems that the authors have tested this on only medium-sized networks, so more benchmarking should be done to ensure its scalability.
john_doe 1 year ago next
Do you think that the proposed reward function was general enough for every neural network paradigm, or does more customization need to be brought in?
ai_engineer_gal 1 year ago next
It appears the authors studied one particular task, so exploring other applications of the approach will be important in assessing its adaptability to various networks.
the_code_dude 1 year ago prev next
Fantastic! This really pushes the boundary in the realm of neural network optimization.
quant_learner 1 year ago next
Agreed, this really feels like a game-changer. Can't wait to experiment with the code.
ml_researcher 1 year ago next
Have you noticed that it takes longer to train than traditional techniques? Given this is a novel area, I wonder if that's an unavoidable trade-off for higher optimization.
quant_learner 1 year ago next
In the research, there are indications that initial wall-clock training time may be higher, but it reduces significantly once we reach convergence. So the training time argument might not hold up entirely.
deep_thinker64 1 year ago next
My experience was that after convergence, the optimization techniques resulting from the differential equation model helped the network generalize better on the unseen data.
the_code_dude 1 year ago next
Interesting, I'd like to test this as well. Can you share some specifics about your experiments, please?
deep_thinker64 1 year ago next
@the_code_dude, of course! I did some simple tests with computer vision classification tasks, and the results were promising. I noticed better generalization vs. traditional training methods.
ml_researcher 1 year ago next
Although this is computer vision focused, I believe differential equation techniques have the potential to improve NLP tasks as well. Excited to see the broader impact!
ai_engineer_gal 1 year ago next
I'm inclined to agree. With NLP's complex directed dependencies and grammatical structures, differential equations might add a useful modeling layer.
john_doe 1 year ago next
I hope research goes further in exploring the benefits and trade-offs for NLP tasks. This really is an exciting direction to take.
student_learner 1 year ago prev next
The adaptive learning rate mentioned in the differential equation model sounds similar to some of the features in the newer Adam Optimizer. Can anyone speak to their relative strengths and weaknesses in practice?
ml_researcher 1 year ago next
The adaptive learning rate in this paper's model is dynamic and relational to the historical context as provided by the differential equation. In contrast, Adam Optimizer samples previous gradients and computes the learning rate based on the exponentially decaying average. Still, experimental comparisons will be useful in understanding their differences.
quant_curious 1 year ago prev next
Will this research directly affect the popular deep learning frameworks very soon? Or would this be a more long-term/future integration?
the_code_dude 1 year ago next
@quant_curious, given that this is quite novel, probable time frames for integration in popular deep learning libraries could span from mid to long-term. Let's keep an eye on the development.
twisted_wires 1 year ago prev next
Paper mentions possible GPU limitations on larger models/training sets. Should we expect more investment in optimizing GPU performance for this type of training?
deep_thinker64 1 year ago next
It's highly likely that this extraordinary approach will spur further interest in optimizing GPU performance for large-scale training. A promising future lies ahead!
rn_learner 1 year ago prev next
Has anyone attempted to combine this approach with recurrent neural networks (RNNs)? Seems like an interesting direction to explore.
ai_engineer_gal 1 year ago next
RNNs will definitely be a fascinating integration with this differential equation approach. Such an endeavor could significantly extend the toolbox for sequence modeling tasks such as language modeling and time series forecasting.
opt_enthusiast 1 year ago prev next
Has any work been done on applying this method to other optimization algorithms like Gradient Descent, RProp or Stochastic Gradient Descent? Would love to learn more about related research.
ml_researcher 1 year ago next
I know of some early works-in-progress which investigate applying this novel approach to other optimization algorithms. The broader scope of differential equation training may have an interesting ripple effect in machine learning optimization, so I encourage everyone to follow these new developments!
algo_curious 1 year ago prev next
Anyone tried implementing this in a distributed computing setup? Seems like training time might heavily benefit.
the_code_dude 1 year ago next
@algo_curious, indeed, this is a valuable approach to reducing training time. Since this method inherently supports historical context, it can be easily integrated into map-reduce-like frameworks.
fascinated_learner 1 year ago prev next
Any alternative suggestions to compare the performance and efficiency of these differential equation-based training methods with regular training methods?
ai_engineer_gal 1 year ago next
You might consider researching projects such as TensorFlow's Optimizer API benchmarks, which have well-developed comparison functions between various optimization methods. These can likely be adapted for this novel differential equation technique, providing a solid foundation for performance evaluation.
open_science 1 year ago prev next
Do the authors plan to make their code open-source? This is an incredible opportunity for the community to engage and build on such groundbreaking research!
ml_researcher 1 year ago next
@open_science, the authors indicated they would publish the code and further research results on their GitHub page once the formal paper was officially accepted. So, stay tuned!
adv_tools 1 year ago prev next
Do these new training techniques enhance any existing library, auto-differentiation tools or does it need a separate framework? What's your take?
the_code_dude 1 year ago next
In theory, the proposed differential equation training should be possible implementing it within current differentiable programming frameworks. However, open-source code will be crucial to assess and determine what changes would be required.
math_lover 1 year ago prev next
Could you specify if the differential equation is of a stochastic or deterministic nature? The discrepancy appears relevant in practice, especially since we have stochastic elements in many neural networks.
ml_researcher 1 year ago next
@math_lover, the referenced differential equation belongs to a deterministic class, but it can still be applied to stochastic neural networks. It indirectly accounts for stochasticity through historical context, although there may be opportunities to incorporate noise directly into the differential equation for enhanced interaction.
optimize_seeker 1 year ago prev next
Has there been any examination of how the new methods compare for likelihood-free inference and variational inference problems?
ai_engineer_gal 1 year ago next
There have been some initial investigations, but the concrete observations regarding differential equation training with likelihood-free inference and variational inference are only emerging in isolated works. I expect multiple research teams to expand on this interesting and interconnected problem set.
code_devil 1 year ago prev next
Can any researchers, proven or budding, share early hints on how to get started with this topic?
john_doe 1 year ago next
@code_devil, a robust starting point would be understanding ordinary differential equations in the context of optimization. I like these resources: [1]
code_devil 1 year ago next
[1] Thanks! I'm eager to explore these resources in-depth!
quant_nerd 1 year ago prev next
When do you think we'll see the transition from traditional training methods to these differential equation techniques?
the_code_dude 1 year ago next
@quant_nerd, the transition will likely be gradual and reliant on more extensive experimentation and benchmarking. Researchers will need to refine these methods and develop compatible tools and frameworks.
math_for_learners 1 year ago prev next
Does the math behind the differential equation techniques have a close relationship with calculus of variations? I wonder if these methods are opening the door to studying neural networks co-opted with such methods.
opt_enthusiast 1 year ago next
The theory behind the differential equation techniques in this study has many practical links to the calculus of variations. In fact, the methods evoke similar principles, like minimizing functionals through an optimization perspective. You can anticipate increasingly advanced combinations of neural networks and calculus-of-variations methods, especially as these differential equation training ideas gain traction.
dl_rookie 1 year ago prev next
Will there be tutorials and accompanying materials, including a theoretical basis like the convergence proof and stability analysis, for this differential equation approach?
ml_researcher 1 year ago next
@dl_rookie, based on prior experiences, once the research matures and is made open source; tutorials and accompanying materials covering the theory and practical elements will become more common. The new methods will require comprehensive documentation for understanding and broader adoption.
science_for_all 1 year ago prev next
This might be a stretch, but any thoughts on using this technique in scientific computing to train large-scale models and complex numerical simulators?
ai_engineer_gal 1 year ago next
@science_for_all, that's a fascinating perspective! Incorporating the novel differential equation approach with large-scale scientific models can benefit simulations and predictions. I suggest following related research on applying advanced optimization techniques to scientific computing problems.