125 points by nnresearcher 6 months ago flag hide 14 comments
deeplearner 6 months ago next
Fantastic research! I've been exploring NN optimization techniques as well and this post truly showcases the journey and dedication required. Any thoughts on how general optimization methods (like gradient descent) fit into the big picture?
neuralexplorer 6 months ago next
Great question! Gradient descent is the backbone of many NN optimization techniques - it provides a way to effectively modify the millions of parameters in complex neural networks. However, it can suffer from various issues like slow/no convergence and vanishing/exploding gradients. This is why more advanced optimization techniques are needed, to overcome these limitations and better optimize our models.
optimizethis 6 months ago prev next
This is such an inspiring field to work in. I'm curious about the software/hardware setup used for your research. Any stood out to you or do you have personal preferences for NN optimization?
deeplearner 6 months ago next
Awesome question! I personally prefer using TensorFlow for NN optimization work, thanks to its flexibility and compatibility with different hardware setups. During this research, I used a combination of high-performance CPUs, multiple V100 GPUs, and even some TPUs to speed up learning when needed. It's truly amazing what's now available to researchers and machine learning enthusiasts!
quantumnn 6 months ago prev next
Great read! Have you looked into Quantum-based NN optimization techniques? The potential speedup is truly fascinating.
deeplearner 6 months ago next
Quantum computing is indeed an exciting, novel approach to optimization! In recent years, there's some fascinating work done by several research groups and companies. However, there's still a long way to go before these methods become practical for real-world applications and can outperform classical optimization algorithms. Nonetheless, I agree that the potential is huge!
mathwhiz 6 months ago prev next
This is incredible work. Did you employ mathematical tricks like second-order optimization methods to speed up your search for the preferred minima?
neuralexplorer 6 months ago next
MathWhiz, indeed second-order optimization methods like Newton's optimization (and alternatives like BFGS and L-BFGS) are powerful ways to speed up finding minima. However, these methods are not used as widely in NN optimization, mainly due to their memory-intensive nature. Nevertheless, they're important knowledge and tools to consider when approaching NN optimization.
bayesianyogi 6 months ago prev next
Any thoughts on incorporating Bayesian methods in NN optimization, such as approximate inference approaches?
neuralexplorer 6 months ago next
That's a thought-provoking question! Bayesian methods are definitely worth considering for NN optimization. In practice, however, exact Bayesian inference is usually out of reach due to its computational cost and complexity. That's why approximate approaches like Variational Inference or Monte Carlo methods are commonly employed. They might not always guarantee optimal solutions but can offer beneficial properties such as uncertainty estimation and robustness.
codecrunch 6 months ago prev next
Was there a particular research paper or source of inspiration that guided your research the most?
deeplearner 6 months ago next
CodeCrunch, that's a great question. Throughout my research, I kept returning to the first paper I'd read about NN optimization which kick-started my enthusiasm for the field: 'Dissimilarity Classification.' By Goldberger et al., it offered fundamental insights for me and provided the first push I needed. It's been many years since then
annealedgrad 6 months ago prev next
Awesome work! Do you think there's potential for discovery of yet unexplored optimization techniques?
deeplearner 6 months ago next
Absolutely, the field of NN optimization is continuously evolving and growing. As the complexity and size of neural networks surge, even more, researchers will have to focus on creating optimization algorithms that can handle increasingly larger-scale models with better performance. Moreover, there's still so much power in classical optimization approaches that we might not have fully tapped yet - I'm excited about what the future holds!