289 points by taneks 6 months ago flag hide 18 comments
deeplearner1 6 months ago next
Great work! I'm curious how much improvement you saw by going from a 100 million to 1 billion parameter model?
deeplearner1 6 months ago next
@hackerengineer We used regularization techniques like dropout and weight decay, as well as early stopping based on validation loss.
hackerengineer 6 months ago prev next
Interesting, I would have thought bigger models would have overfitting issues. How'd you mitigate that?
mlresearcher 6 months ago next
Bigger models tend to have more capacity to fit complex functions and generalize better, given enough data and regularization.
anonymous 6 months ago prev next
Did you try other model architectures, like capsule networks or transformers?
deeplearner1 6 months ago next
Yes, we experimented with various architectures, and this one produced the best results on our task.
gpuenthusiast 6 months ago prev next
How long did it take to train this model? I imagine you needed some heavy hardware.
deeplearner1 6 months ago next
@gpuenthusiast Correct, we used 8 V100 GPUs and trained the model for about a week. It's a trade-off between wall time and resource usage.
optimizationguru 6 months ago prev next
Which optimization methods and learning rate schedules did you use?
deeplearner1 6 months ago next
@optimizationguru We used Adam with a cosine annealing learning rate scheduler, and it worked well for our case.
randusername 6 months ago prev next
How did your results compare to state-of-the-art models? Did you do extensive benchmarks?
deeplearner1 6 months ago next
@randusername Yes, we did extensive benchmarks and our model outperformed the state-of-the-art models by a considerable margin.
figurativehat 6 months ago prev next
What was your approach in data preprocessing and augmentation?
deeplearner1 6 months ago next
@figurativehat We followed the common preprocessing and augmentation techniques in our task, and made sure to have diverse data sets.
curiousx 6 months ago prev next
Are there any limitations to your methodology or potential improvements you see for future work?
deeplearner1 6 months ago next
@curiousx There's definitely room for improvement, such as reducing training time, employing more advanced techniques, and exploring other architectures. challenging the assumptions in the data set could also be a promising direction.
quietdev 6 months ago prev next
Any open source plans so we can try the model ourselves or replicate your experiments?
deeplearner1 6 months ago next
@quietdev We plan to open source our codebase and a pretrained model, and will post an update on its availability soon!