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.
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.
Example solution: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Example explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Problem: In this work, we propose the Sparse Deep Scattering Croise Network (SDCSN) a novel architecture based on the Deep Scattering Network (DSN). The DSN is achieved by cascading wavelet transform convolutions with a complex modulus and a time-invariant operator. We extend this work by first,
crossing multiple wavelet family transforms to increase the feature diversity while avoiding any learning. Thus providing a more informative latent representation and benefit from the development of highly specialized wavelet filters over the last decades. Beside, by combining all the different wavelet representations, we reduce the amount of prior information needed regarding the signals at hand.
Secondly, we develop an optimal thresholding strategy for over-complete filter banks that regularizes the network and controls instabilities such as inherent non-stationary noise in the signal. Our systematic and principled solution sparsifies the latent representation of the network by acting as a local mask distinguishing between activity and noise. Thus, we propose to enhance the DSN by increasing the variance of the scattering coefficients representation as well as improve its robustness with respect to non-stationary noise.
We show that our new approach is more robust and outperforms the DSN on a bird detection task.
Solution: We propose to enhance the Deep Scattering Network in order to improve control and stability of any given machine learning pipeline by proposing a continuous wavelet thresholding scheme