In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Solution is here: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Now, solve this: Human brain function as measured by functional magnetic resonance imaging
(fMRI), exhibits a rich diversity. In response, understanding the individual variability
of brain function and its association with behavior has become one of the
major concerns in modern cognitive neuroscience. Our work is motivated by the
view that generative models provide a useful tool for understanding this variability.
To this end, this manuscript presents two novel generative models trained
on real neuroimaging data which synthesize task-dependent functional brain images.
Brain images are high dimensional tensors which exhibit structured spatial
correlations. Thus, both models are 3D conditional Generative Adversarial networks
(GANs) which apply Convolutional Neural Networks (CNNs) to learn an
abstraction of brain image representations. Our results show that the generated
brain images are diverse, yet task dependent. In addition to qualitative evaluation,
we utilize the generated synthetic brain volumes as additional training data to improve
downstream fMRI classifiers (also known as decoding, or brain reading).
Our approach achieves significant improvements for a variety of datasets, classifi-
cation tasks and evaluation scores. Our classification results provide a quantitative
evaluation of the quality of the generated images, and also serve as an additional
contribution of this manuscript.
Solution:
Two novel GANs are constructed to generate high-quality 3D fMRI brain images and synthetic brain images greatly help to improve downstream classification tasks.