Detailed Instructions: 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.
See one example below:
Problem: 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: 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.

Problem: Deep learning yields great results across many fields,
 from speech recognition, image classification, to translation.
 But for each problem, getting a deep model to work well involves
 research into the architecture and a long period of tuning.

 We present a single model that yields good results on a number
 of problems spanning multiple domains. In particular, this single model
 is trained concurrently on ImageNet, multiple translation tasks,
 image captioning (COCO dataset), a speech recognition corpus,
 and an English parsing task. 

 Our model architecture incorporates building blocks from multiple
 domains. It contains convolutional layers, an attention mechanism,
 and sparsely-gated layers.

 Each of these computational blocks is crucial for a subset of
 the tasks we train on. Interestingly, even if a block is not
 crucial for a task, we observe that adding it never hurts performance
 and in most cases improves it on all tasks.

 We also show that tasks with less data benefit largely from joint
 training with other tasks, while performance on large tasks degrades
 only slightly if at all.
Solution:
Large scale multi-task architecture solves ImageNet and translation together and shows transfer learning.