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: Lifelong machine learning focuses on adapting to novel tasks without forgetting the old tasks, whereas few-shot learning strives to learn a single task given a small amount of data. These two different research areas are crucial for artificial general intelligence, however, their existing studies have somehow assumed some impractical settings when training the models. For lifelong learning, the nature (or the quantity) of incoming tasks during inference time is assumed to be known at training time. As for few-shot learning, it is commonly assumed that a large number of tasks is available during training. Humans, on the other hand, can perform these learning tasks without regard to the aforementioned assumptions. Inspired by how the human brain works, we propose a novel model, called the Slow Thinking to Learn (STL), that makes sophisticated (and slightly slower) predictions by iteratively considering interactions between current and previously seen tasks at runtime. Having conducted experiments, the results empirically demonstrate the effectiveness of STL for more realistic lifelong and few-shot learning settings.
Solution:
This paper studies the interactions between the fast-learning and slow-prediction models and demonstrate how such interactions can improve machine capability to solve the joint lifelong and few-shot learning problems.