Neural networks are becoming massive monsters that are hard to train (without the “regular” 12 last-generation GPUs).
Is there a way to skip that? Let me introduce you to Zero-Cost proxies
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References
- https://www.technologyreview.com/2022/08/05/1056814/automation-ai-machine-learning-automl/
- https://iclr-blog-track.github.io/2022/03/25/zero-cost-proxies/
Transcript
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And here we are again with season four of the Data Science at Home podcast.
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Ambassadors are volunteers who are passionate about data science and want to give back to our growing community of data science professionals and enthusiasts.
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You will be instrumental in helping us achieve our goal of raising awareness about the critical role of data science in cutting edge technologies.
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If you want to learn more about this program, visit the Ambassadors page on our website at datascience@home.com.
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Welcome back to another episode of Data Science at Home podcast.
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I’m from Chesco podcasting from the usual office of Ameth Technologies based in Belgium.
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In this episode, I want to discuss some of the automated techniques that are used to develop artificial intelligence.
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Due to the fact that as artificial intelligence becomes more and more, let’s say, off the shelf in a way, many of the algorithms that were essentially became the core of artificial intelligence and in particular in the deep learning field have now become some sort of state of the art and quite standardized ways of producing artificial intelligence or whatever you want to call it.
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But essentially due to the fact that there are so many moving variables, for example, the topology of the network, as well as the number of inputs, as well as the number of layers, and then of course, the learning rate and all the other hyper parameters that you might think of when you design a deep learning model.
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Well, due to all these moving pieces, in fact, it’s become kind of an art, dark art to design the best possible topology that will solve that particular problem with that particular data.
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And in fact, there is no such formula that would allow researchers or practitioners in this case to understand what type of topology one should be using or what’s the percentage of drop out for a particular use case, if not by trial and error.
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That is, of course, running these models over and over again and then apply tiny changes or more or less targeted changes to the topology and to all the other variables.
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And essentially that means moving into a dimensional space that is even bigger than the dimensional space of neural networks, which is already very high if you consider a number of parameters of a different network today in pretty much any domain.
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Of course, I’m not generalizing, I’m just trying to give you some tangible numbers.
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A network with several millions of parameters nowadays is no longer a big deal.
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We have been speaking about the monsters or the bisques of deep learning, like 100 billion parameters, 175 parameters, and we remember the family of GPT models and Dolly and all the other big models that allow us to pretty much have fun online or just speak about deep learning in action.
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Well, these are monsters and except for these monsters which means that they are in the realm of hundreds of billions of parameters all the rest are pretty much really tiny with respect to those monsters but still pretty large with respect to, let’s say off the shelf machine learning models.
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Don’t forget that of course a very sophisticated or a very deep random forest model would be orders of magnitude smaller than one of the simplest neural networks out there.
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Now of course they would be applied to different use cases, to different types of types of data and also they might be giving different results depending on, for example for a computer vision system you would not definitely not use a random force.
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But just to give you an idea of what’s the dimension of these models nowadays and there is a problem when these things become large.
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Well, there is a problem for practitioners because how do you design something? Is there a tool that would allow you to let’s say, facilitate or give you support in deciding how big or small a particular network should be? How many layers should that network have? And that’s when AutoML was born.
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AutoML in fact AutoML algorithms operate at a level of abstraction that is usually above the machine learning models.
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Of course they can be applied to pretty much all machine learning models, not just neural networks.
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And they usually rely only on the outputs of the model as a guide to guide and suggest to the practitioner that maybe some, let’s say hyper parameters specific to that particular model should be changed.
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The problem is that when it comes to neural networks this process is very time consuming and also burns a lot of resources.
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And I think we have discussed this long time ago nas which stands for Neural Architecture Search.
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There is a technique that allows researchers or practitioners to find out, to discover the best possible topology or neural network for a particular scenario.
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And how do they do that? Well, by iterating over and over again observing the outputs, observing some metrics of the training session and understand, measuring essentially if the accuracy is moving and what’s the magnitude of that movement in particular observed directly from the output.
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The problem is that as I said this for a relatively large neural network this becomes sometimes prohibitive actually many more times than sometimes it becomes really prohibitive when as the networks grow in size and also as the data set grows in volume.
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So as you can understand it would be great to have for example a technique or a method that allows you to let’s say, understand or predict what would be the accuracy of a particular neural network of which I’ve changed to the topology, I’ve changed some of the other parameters.
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But I would like to know that accuracy before training, maybe without training or training an infinitesimal amount of data with respect to what I usually do under the Nas technique.
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Right? And this is how zero cost proxies have been introduced.
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There is a very interesting literature.
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I was pretty unaware of it.
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But I’ve spent some time trying to do some research of what has been published so far and I should say that indeed in the last few years.
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In fact it’s a pretty new methodology or trend in a way that I believe is raised due to the fact that indeed we need to optimize a lot of the training process.
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That there’s a lot of room for improvement there.
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We have to save as much as we can computation for the sake of the environment, for the sake of the costs.
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Training and retraining this neural network can cost several millions of dollars when you think about the big monsters that I mentioned at the beginning of the episode.
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And so zero cost proxies indeed can, at least from a theoretical perspective, they can solve such a problem, which is the problem of avoiding training the network almost entirely and still understand or predict if that network is going to perform, then change some hyper parameters and run that prediction again in pretty much zero time.
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That’s why zero cost and understand if that topology indeed would be better than the previous one.
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So this means that the dimensional space of the problem would be essentially demolished because instead of being in a dimensional space that considers this space of the upper parameters times the space of the usual parameter space of the network, that is, the number of parameters, the weights, etc.
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For in this case, with zero cost we would just be in front of the hyper parameter dimensional space which is notoriously much smaller, of course, than the parameter space even though there is an infinite number of combinations in the neural networks.
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How to set the topology of the neural network? Of course, finding a suboptimal index space would be much, much easier than finding a suboptimal in the parameter space with millions and millions and sometimes billions of characters.
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So there is a taxonomy around the zero cost proxies and of course I will provide some of the links that I have been exploring in these days which are very interesting and they require some time to digest all that information.
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But I think it’s worth it.
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I learned a lot of stuff some other times.
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Some papers don’t add anything super novel but they help, or at least that happened with me.
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They helped me understanding the background and some of the linear algebra metrics that usually researchers are considering for zero cost proxies.
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So without entering the details, which can be quite intense for a podcast episode, zero cost proxies have an extremely quick way to estimate the performance of neural network architectures.
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And so the method essentially computes statistics from usually a forward pass of a single mini batch of data.
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And this means that in fact, it’s not exactly zero cost.
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It’s really negligible with respect to the entire training process.
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That’s why it’s zero.
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Metaphorically speaking.
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If you want to say zero from an engineering standpoint, of course it’s not.
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But as I said, it’s nothing with respect to what the neural network should train for real.
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Now, of course there are different groups or different ways to, let’s say, categorize zillow cost proxies.
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Of course the two major ones are data independent and data dependent.
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And based on these two, let’s say, categories, they measure different things, of course, because in the first group, of course, we have zero cost proxies that are data independent which means that they don’t rely on the data set, on the input data set to measure the quality of a particular network.
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So one would say how does that happen? Like how can a method ignore the data completely to understand if a particular network is in fact better than another? And well, there are some techniques that use, for example, synthetic proxy tasks to estimate the ability of the particular architecture to capture, for example, different types of sign frequencies to capture, scaling variances, special information.
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So if you are dealing with, for example, a problem in which all these things are indeed involved, for example, special information can be involved for several things, even computer vision as well as GIS systems, scaling variances for images for sure and sign frequencies for some sort of encoding.
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But usually these synthetic proxy tasks are usually present in the natural, let’s say, environment.
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So when you have a network that is indeed being trained for a particular use case, most of the time the network has to have the ability to capture sign frequencies, scale invariances and special information.
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So researchers understood that, we all know that in fact, and said okay, how about creating these things synthetically and evaluate without data? Another very important metric that is used is, of course the number of parameters in the network.
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And yet another one is a way to approximate the neural network by piecewise linear functions that are usually conditioned on the activation patterns of the network.
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So these are all of course, synthetic measures.
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They are exasperated in a way during training, during the assessment of the quality of the neural network.
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And that’s why we call this zero cost proxies data independent because they do not need the presence of the data.
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The second category, as you can imagine, is the data dependent zero cost proxies.
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And here is where of course, the methodology considers the initial data, the input data.
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Now of course, when we say input data again, it’s a tiny fraction of the entire data set because we still want the methods to be a zero cost approach, which means that we still need to have a negligible time that passes to understand and to assess these metrics.
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And yet the metrics can be completely different.
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For example, there are techniques that measure the intra and inter class correlations of the prediction.
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Jacobian Mattresses there are of course number of Flops floating point operations to pass the input through the network.
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That gives already an idea of how complex the network is from the input to the output layer.
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There is a technique that sums the Euclidean norm of the gradients.
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Another technique that performs an approximation of the neural network gaussian process using Monte Carlo methods, which is usually a much cheaper measure of performance and so on and so forth.
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So these are more techniques that look at of course, the data in the sense that they let the data pass through in a forward pass, usually very minimal amount of batches, usually one.
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And then they start calculating metrics that depend of course, on the particular methodology.
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Now, does this work? Well, there is a very interesting work, kind of a review of all these methods in action.
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And some researchers have come to the conclusion that across a wide range of tasks there is no single zero cost proxy that performs significantly better than the others.
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So pretty much they are performing the same statistically.
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Of course, another point that is quite important to mention is that zero cost proxies still require research because Flops floating point operations, a number of parameters alone are usually consistent, are usually quite competitive baselines.
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So one would say why should I complicate my life with a zero cost proxy methodology? If number of floating point operations and number of parameters are both a pretty good baseline, they give me a decent measure of how that network is performing or will perform.
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So of course, my conclusion is that at least from the literature I’ve been reading is that zero cost proxies are an interesting approach.
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The idea, of course is amazing.
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It’s the execution that probably doesn’t match the idea or the power of the idea, which is great and that happens in research.
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But I believe that probably using zero cost proxies together with other methods, for example, model based predictions usually or one shot training as well, maybe they can help improving the performance of the Nas neural architecture search techniques that are already in place or they can just give some more robustness to those existing methodologies.
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Of course, I’m not an expert, I admit that I’m just reframing a bit the literature that I’ve been exploring in the last few days or weeks.
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It has been a nice journey, to be honest.
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Very interesting stuff I will find as interesting as I did, of course.
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That’s it for today.
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