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.
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Question: Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.

Answer: Ideal methodology to inject noise to input data during CNN training

Question: In deep learning, performance is strongly affected by the choice of architecture
and hyperparameters. While there has been extensive work on automatic hyperpa-
rameter optimization for simple spaces, complex spaces such as the space of deep
architectures remain largely unexplored. As a result, the choice of architecture is
done manually by the human expert through a slow trial and error process guided
mainly by intuition. In this paper we describe a framework for automatically
designing and training deep models. We propose an extensible and modular lan-
guage that allows the human expert to compactly represent complex search spaces
over architectures and their hyperparameters. The resulting search spaces are tree-
structured and therefore easy to traverse. Models can be automatically compiled to
computational graphs once values for all hyperparameters have been chosen. We
can leverage the structure of the search space to introduce different model search
algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen-
tial model-based optimization (SMBO). We present experiments comparing the
different algorithms on CIFAR-10 and show that MCTS and SMBO outperform
random search. We also present experiments on MNIST, showing that the same
search space achieves near state-of-the-art performance with a few samples. These
experiments show that our framework can be used effectively for model discov-
ery, as it is possible to describe expressive search spaces and discover competitive
models without much effort from the human expert. Code for our framework and
experiments has been made publicly available

Answer: We describe a modular and composable language for describing expressive search spaces over architectures and simple model search algorithms applied to these search spaces.

Question: The recent development of Natural Language Processing (NLP) has achieved great success using large pre-trained models with hundreds of millions of parameters. However, these models suffer from the heavy model size and high latency such that we cannot directly deploy them to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. Like BERT, MobileBERT is task-agnostic; that is, it can be universally applied to various downstream NLP tasks via fine-tuning. MobileBERT is a slimmed version of BERT-LARGE augmented with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we use a bottom-to-top progressive scheme to transfer the intrinsic knowledge of a specially designed Inverted Bottleneck BERT-LARGE teacher to it. Empirical studies show that MobileBERT is 4.3x smaller and 4.0x faster than original BERT-BASE while achieving competitive results on well-known NLP benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves 0.6 GLUE score performance degradation, and 367 ms latency on a Pixel 3 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a 90.0/79.2 dev F1 score, which is 1.5/2.1 higher than BERT-BASE.

Answer:
We develop a task-agnosticlly compressed BERT, which is 4.3x smaller and 4.0x faster than BERT-BASE while achieving competitive performance on GLUE and SQuAD.