# Tag Info

## Hot answers tagged hyperparameter-optimization

7

There is a technique called Pruning in neural networks, which is used just for this same purpose. The pruning is done on the number of hidden layers. The process is very similar to the pruning process of decision trees. The pruning process is done as follows: Train a large, densely connected, network with a standard training algorithm Examine the trained ...

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You could say that NAS fits into the domain of Meta Learning or Meta Machine learning. I've pulled the NAS papers from my notes, this is a collection of papers/lectures that I personally found very interesting. It's sorted in rough chronological descending order, and *** means influential / must read. Quoc V. Le and Barret Zoph are to good authors on the ...

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The difference between the validation and test set in my opinion should be explained in this way: the validation set is meant to be used multiple times. the test set is meant to be used only once. I think that the misunderstanding here arise because machine learning is mostly taught focusing only on a specific part of a large pipeline, which is the model ...

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A surrogate model is a simplified model. It is a mapping $y_S=f_S(x)$ that approximates the original model $y=f(x)$, in a given domain, reasonably well. Source: Engineering Design via Surrogate Modelling: A Practical Guide In the context of Bayesian optimization, one wants to optimize a function $y=f(x)$ which is expensive (very time consuming) to evaluate, ...

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Yes. Usually you would use cross validation to avoid overfitting during parameter tuning. If your dataset is large enough, and you don't try too many parameter combinations, this will work well, because to "get lucky" and overfit, a parameter combination will need to work very well on many variations of the problem, which is less likely than working well on ...

3

The book Deep Learning by Goodfellow, Bengio, and Courville says (Sec 8.3.3, p 292 in my copy) states that Unfortunately, in the stochastic gradient case, Nesterov momentum does not improve the rate of convergence. I'm not sure why this is, but the theoretical advantage depends on a convex problem, and from this, it sounds like the practical advantage ...

2

Yes, your understanding of the hidden state is correct. But the size of the hidden state is a hyperparameter that needs to found by trial-and-error. There is no closed-form formula or solution which links the size of the hidden state and the problem at hand. But, there are some rules of thumb like to start out with the size of the hidden state to be a power ...

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How to find the best configuration for an algorithm is an open research question in AI. The topic in general is known as `hyper-parameter optimization' and there are a range of possible methods: One of the most popular is IRace, but other possibilities include: Spearmint: uses wrappers in Matlab or Python. It uses MongoDb, and Bayesian optimisation ...

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tl;dr The safest method I've found is to use cross-validation for hyperparameter selection and a hold-out test set for a final evaluation. Why this isn't working for you... In your case, I suspect you're either running a large number of iterations during for hyperparameter selection or you have a fairly small dataset (or even a combination of both). If you ...

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The theory behind hyper-parameter optimization (HPO) is not well developed. Nonetheless, there are several hyper-parameter optimization approaches, such as Bayesian optimization (using Gaussian processes), random search, grid search, genetic algorithms, etc. See, for example, the paper Hyperparameter Search in Machine Learning (2015), which attempts to ...

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After you've computed $h^{1}_{optimal}$ the only thing you can be sure is that this is the best (assuming constrained case) value of $h^1$ (with respect to some model performance metric) given your initial values for $h^2, ..., h^n$. If you change a bit any of $h^2, ..., h^n$ you're no longer certain that the value $h^1$ you found is the optimal one. So yes, ...

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Such a system can and does solve the problem of hyperparameter tuning. Google's AutoML does this. Here is another example that uses a Genetic Algorithm to breed new neural network structures. AutoML has been shown to outperform humans in the rate that it improves network designs. It seems to favour Residual Network style topologies.

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If you used your five $X_{test}$ sets multiple times (to measure the average AUC) to decide on the best set of hyperparameters (i.e. optimizer, learning rate, batch size, dropout, activation) then yes, you successfully conducted hyper-parameter optimization. However, the AUC you received for the best set of hyperparameters found (by manual tuning) is not ...

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Short answer: training "duration" or number of epochs/updates should be cross-validated too: you want to early-stop your training to prevent overfitting. Longer answer: Think of accuracy on the validation set as an estimate of accuracy on future data, given the value of some hyperparameter. In this case, the hyperparameter of interest is the number of ...

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Simply stated, you use your validation set to regularize your model for unseen data. Test data is completely unseen data, on which you evaluate your model. Various validation strategies are used to improve your model to perform for unseen data. So strategies like k-fold cross-validation are used. Also, the validation set helps you in tuning your ...

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PCA works well where data sample space is linear. If data sample space is not linear or it is manifold data then model without PCA may perform better than model using PCA. In the given image you can see, data is manifold. In this type of data, PCA, which is based on projection technique does not work well. That's why we use manifold learning technique to ...

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PCA can make models worse, imagine data points scattered along two elongated parallel rectangles. The axis with the greatest variation will be parallel to the rectangles but doesn't provide any benefit in classifying the points.

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This is one of the most difficult and unsolved problems in machine learning and deep learning! There are many different ways to estimate the most appropriate hyper-parameters, such as grid search, random search, Bayesian optimization, meta-learning, reinforcement learning, and evolutionary algorithms (e.g. NEAT). However, the problem is that most if not ...

1

The importance of having a totally separate test set is very crucial. Once you start to use the validation set performance as a measure to use to tune hyper parameters you are biasing your network to work well on the validation set so it can no longer be relied on as a true measure of performance. Eventually if you use your test set too often then adjust ...

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This is a standard ML problem: changing hyper-parameters changes the performance of the whole model. Ideally, you'd be cross-validating hyper-parameter choices, not merely comparing on a static validation set. That being said you need to be careful with hyper-parameter optimization because you could overfit these to the peculiarities of your validation set ; ...

1

I have consistently found Adam to work very well but to tell you the truth I have not seen all that much difference in performance based on the optimizer. Other factor seem to have much more influence on the final model performance.In particular adjusting the learning rate during training can be very effective. Also saving the weights for the lowest ...

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What is Bayesian optimization? Introduction Bayesian optimization (BO) is an optimization technique used to model an unknown (usually continuous) function $f: \mathbb{R}^d \rightarrow Y$, where typically $d \leq 20$, so it can be used to solve regression and classification problems, where you want to find an approximation of $f$. In this sense, BO is ...

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See here for a potential way to do it: http://infinity77.net/global_optimization/#motivation-motivation http://infinity77.net/global_optimization/#rules-the-rules You basically test the two (or more) optimization algorithms against known objective functions, with several random (but repeatable) starting points and then analyze the outcome.

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Here are two review articles: Elsken, Metzen, Hutter: Neural Architecture Search: A Survey (2019), Journal of Machine Learning Research 20, 1-21 He, Zhao, Chu: AutoML: A Survey of the State-of-the-Art (2019)

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Yes, you can also automate the choice of certain hyperparameters of the evolutionary algorithm. In this context, this process is called self-adaptation. There are different ways of performing self-adaptation (depending on the hyper-parameter that needs to self-adapt). See e.g. the chapter Self-Adaptation in Evolutionary Algorithms (by Silja Meyer-Nieberg and ...

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Welcome to AI.SE Enes. I think by random search, you are referring to so-called "black-box optimization". Random search is sometimes used as a name for this, but BBO is a more common name, and might be easier to search for. There are many BBO techniques. 'random search' is usually used to refer to a hill-climbing algorithm where you start at a random ...

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Apart from the Scikit-Optimize package related to Scikit-Learn, following are some of the packages related to Bayesian optimization: GPyOpt pyGPGO Hyperopt bayesian-optimization safeopt RoBO

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You can take a look at bayesian hyperparameter optimization as a general method of optimizing loss (or anything) as a function of the hyperparameters. But note that in general the deeper your network the better, so optimizing loss as a function of number of layers isn't a very fun thing to do. Grid search and a bit of common sense (as learnt by seeing many ...

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