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.

[EX Q]: Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces. Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT)  for loop invariants from program execution traces. We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently. We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset. CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset. Moreover, CLN2INV takes only 1.1 second on average for each problem, which is 40 times faster than existing approaches. We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset.
[EX A]: We introduce the Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants and general SMT formulas.

[EX Q]: Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by regularizing the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior. Previous work has taken important steps to connect these topics through various forms of gradient regularization. We find, however, that existing methods that use attributions to align a model's behavior with human intuition are ineffective. We develop an efficient and theoretically grounded feature attribution method, expected gradients, and a novel framework, attribution priors, to enforce prior expectations about a model's behavior during training. We demonstrate that attribution priors are broadly applicable by instantiating them on three different types of data: image data, gene expression data, and health care data. Our experiments show that models trained with attribution priors are more intuitive and achieve better generalization performance than both equivalent baselines and existing methods to regularize model behavior.
[EX A]: A method for encouraging axiomatic feature attributions of a deep model to match human intuition.

[EX Q]: Effective training of neural networks requires much data. In the low-data regime,
 parameters are underdetermined, and learnt networks generalise poorly. Data
 Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data
 more effectively. However standard data augmentation produces only limited
 plausible alternative data. Given there is potential to generate a much broader set
 of augmentations, we design and train a generative model to do data augmentation.
 The model, based on image conditional Generative Adversarial Networks, takes
 data from a source domain and learns to take any data item and generalise it
 to generate other within-class data items. As this generative process does not
 depend on the classes themselves, it can be applied to novel unseen classes of data.
 We show that a Data Augmentation Generative Adversarial Network (DAGAN)
 augments standard vanilla classifiers well. We also show a DAGAN can enhance
 few-shot learning systems such as Matching Networks. We demonstrate these
 approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and
 VGG-Face data. In our experiments we can see over 13% increase in accuracy in
 the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%
 to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we
 observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in
 EMNIST (from 59.5% to 61.3%).
[EX A]:
Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distance