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There Will Be a Scientific Theory of Deep Learning
51–60 of 179 posts
Re: There Will Be a Scientific Theory of Deep Learning
#52Is there not some Rice's Theorem equivalent for deep nets? After all they are machines that are randomly generated, so from classical computer science I would not presume a theory of "what do all deep nets do" to be prima facie logically possible. Nor do I see this explained in the objections section.
Re: There Will Be a Scientific Theory of Deep Learning
#53As someone who works in the area, this provides a decent summary of the most popular research items. The most useful and impressive part is the set of open problems at the end, which just about covers all of the main research directions in the field. The skepticism I'm seeing in the comments really highlights how little of this work is trickling down to the public, which is very sad to see. While it can offer few mat…
"why do neural networks work better than other models?" That sounds really interesting - any references (for a non specialist)?
Re: There Will Be a Scientific Theory of Deep Learning
#54Earlier quoted context omitted.
The inflection point was 2012, when AlexNet [0], a deep convolutional neural net, achieved a step-change improvement in the ImageNet classification competition. After seeing AlexNet’s results, all of the major ML imaging labs switched to deep CNNs, and other approaches almost completely disappeared from SOTA imaging competitions. Over the next few years, deep neural networks took over in other ML domains as well. The…
Comparing Deep Learning with neuroscience may turn out to be erroneous. They may be orthogonal. The brain likely has more in common with Reservoir Computing (sans the actual learning algorithm) than Deep Learning. Deep Learning relies on end to end loss optimization, something which is much more powerful than anything the brain can be doing. But the end-to-end limitation is restricting, credit assignment is a big pro…
> If only we could train a model to just use Photoshop directly, but we can't.
It is probably coming, I get the impression - just from following the trend of the progress - that internal world models are the hardest part. I was playing with Gemma 4 and it seemed to have a remarkable amount of trouble with the idea of going from its house to another house, collecting something and returning; starting part-way through where it was already at house #2. It figured it out but it seemed to be working very hard with the concept to a degree that was really a bit comical.
It looks like that issue is solving itself as text & image models start to unify and they get more video-based data that makes the object-oriented nature of physical reality obvious. Understanding spatial layouts seems like it might be a prerequisite to being able to consistently set up a scene in Photoshop. It is a bit weird that it seems pulling an image fully formed from the aether is statistically easier than putting it together piece by piece.
Re: There Will Be a Scientific Theory of Deep Learning
#55Wait a min. Does this paper says we don't know how back-propogation works?
Re: There Will Be a Scientific Theory of Deep Learning
#56Here's where I'm missing understanding: for decades the idea of neural networks had existed with minimal attention. Then in 2017 Attention Is All You Need gets released and since then there is an exponential explosion in deep learning. I understand that deep learning is accelerated by GPUs but the concept of a transformer could have been used on much slower hardware much earlier.
This video gives a great overview of the history of the acceleration:
https://youtu.be/glWvwvhZkQ8?si=-HGtfd_KHYfatEQ
Although it’s focused on Ilya, some great history is covered.
Re: There Will Be a Scientific Theory of Deep Learning
#57Here's where I'm missing understanding: for decades the idea of neural networks had existed with minimal attention. Then in 2017 Attention Is All You Need gets released and since then there is an exponential explosion in deep learning. I understand that deep learning is accelerated by GPUs but the concept of a transformer could have been used on much slower hardware much earlier.
The inflection point was 2012, when AlexNet [0], a deep convolutional neural net, achieved a step-change improvement in the ImageNet classification competition. After seeing AlexNet’s results, all of the major ML imaging labs switched to deep CNNs, and other approaches almost completely disappeared from SOTA imaging competitions. Over the next few years, deep neural networks took over in other ML domains as well. The…
Indeed. I would add a third factor to compute and datasets: the lego-like aspect of NN that enabled scalable OSS DL frameworks.
I did some ML in mid 2000s, and it was a PITA to reuse other people code (when available at all). You had some well known libraries for SVM, for HMM you had to use HTK that had a weird license, and otherwise looking at experiments required you to reimplement stuff yourself.
Late 2000s had a lot of practical innovation that democratized ML: theano and then tf/keras/pytorch for DL, scikit learn for ML, etc. That ended up being important because you need a lot of tricks to make this work on top of "textbook" implementation. E.g. if you implement EM algo for GMM, you need to do it in the log space to avoid underflow, DL as well (gorot and co initialization, etc.).
Re: There Will Be a Scientific Theory of Deep Learning
#58This is encouraging. The title is a bit much. "Potential points of attack for understanding what deep learning is really doing" would be more accurate but less attention-grabbing. It might lead to understanding how to measure when a deep learning system is making stuff up or hallucinating. That would have a huge payoff. Until we get that, deep learning systems are limited to tasks where the consequences of outputting…
> measure when a deep learning system is making stuff up or hallucinating That's a great problem to solve! (Maybe biased, because this is my primary research direction). One popular approach is OOD detection, but this always seemed ill-posed to me. My colleagues and I have been approaching this from a more fundamental direction using measures of model misspecification, but this is admittedly niche because it is very…
> Could still be a while before a breakthrough comes from any direction.
It would be valuable enough that getting significant funding to work on it is probably possible. Especially with all the money being thrown at AI.
Re: There Will Be a Scientific Theory of Deep Learning
#59As someone who works in the area, this provides a decent summary of the most popular research items. The most useful and impressive part is the set of open problems at the end, which just about covers all of the main research directions in the field. The skepticism I'm seeing in the comments really highlights how little of this work is trickling down to the public, which is very sad to see. While it can offer few mat…
> why do neural networks work better than other models
The only people for whom this is an open question are the academics - everyone else understands it's entirely because of the bagillions of parameters.
Re: There Will Be a Scientific Theory of Deep Learning
#60As someone who works in the area, this provides a decent summary of the most popular research items. The most useful and impressive part is the set of open problems at the end, which just about covers all of the main research directions in the field. The skepticism I'm seeing in the comments really highlights how little of this work is trickling down to the public, which is very sad to see. While it can offer few mat…
"why do neural networks work better than other models?" That sounds really interesting - any references (for a non specialist)?
https://en.wikipedia.org/wiki/Universal_approximation_theore...
the better question is why does gradient descent work for them