Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations.
In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training.
In contrast to existing methods, we consider this task as a ranking and transfer learning problem.
We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other data sets, our model is able to effectively rank learning curves without having to observe many or very long learning curves.
We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture.
In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.
A method commonly used by human experts to speed up the optimization of neural architectures or hyperparameters is the early termination of iterative training processes that are unlikely to improve the current solution.
A common technique to determine the likelihood of no improvement is to compare the learning curve of a new configuration to the one of the currently best configuration.
This idea can also be used to speed up automated machine learning processes.
For this purpose, it is common practice to extrapolate the partial learning curve in order to predict the final performance of the currently investigated model.
Current extrapolation techniques have several weaknesses that make them unable to realize their full potential in practice.
Many of the methods require sufficient sample learning curves to make reliable predictions (Chandrashekaran & Lane, 2017; Klein et al., 2017; Baker et al., 2018) .
Thus, the extrapolation method for the first candidates can not be used yet, which means more computational effort.
Other methods do not have this disadvantage, but require sufficiently long learning curves to make reliable predictions which again means unnecessary overhead (Domhan et al., 2015) .
Many of these methods also do not take into account other information such as the hyperparameters of the model being examined or its network architecture.
We address the need for sample learning curves by devising a transfer learning technique that uses learning curves from other problems.
Since the range of accuracy varies from data set to data set, we are forced to consider this in our modeling.
But since we are not interested in predicting the performance of a model anyway, we use a ranking model that models the probability that the model currently being investigated surpasses the best solution so far.
This does not only solve the problem but also provides a better modeling of the actual task.
In order to be able to make reliable predictions for short learning curves, we consider further characteristics of the model such as its network architecture.
We compare our ranking method with respect to a ranking measure against different methods on five different image classification data sets.
We also show that our method is capable of significantly accelerating a neural architecture search.
Furthermore, we conduct several ablation studies to provide a better motivation of our model and its behavior.
In this paper we present LCRankNet, a method to automatically terminate unpromising model configurations early.
The two main novelties of the underlying model are that it is able to consider learning curves from other data sets and that it uses a pairwise ranking loss.
The former allows to predict for relatively short, and in extreme cases even without, learning curves.
The latter directly allows to model the probability that one configuration is better than the another.
We analyze our method on five different data sets against three alternatives.
In an experiment to optimize network architectures, we obtain the fastest results.
In the best case, LCRankNet is 100 times faster without sacrificing accuracy.
We also examine the components and predictions of our method to give the reader a better understanding of the design choices and functionalities.
Learn to rank learning curves in order to stop unpromising training jobs early. Novelty: use of pairwise ranking loss to directly model the probability of improving and transfer learning across data sets to reduce required training data.