instruction:
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.
question:
Developing agents that can learn to follow natural language instructions has been an emerging research direction. While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans. To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks. We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task. Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy. We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.
answer:
We propose a modular framework that can accomplish tasks specified by programs and achieve zero-shot generalization to more complex tasks.


question:
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%).
answer:
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


question:
Machine learning models including traditional models and neural networks can be easily fooled by adversarial examples which are generated from the natural examples with small perturbations.   This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection.   Unfortunately, even state-of-the-art defense approaches such as adversarial training and defensive distillation still suffer from major limitations and can be circumvented.   From a unique angle, we propose to investigate two important research questions in this paper: Are adversarial examples distinguishable from natural examples?   Are adversarial examples generated by different methods distinguishable from each other?   These two questions concern the distinguishability of adversarial examples.   Answering them will potentially lead to a simple yet effective approach, termed as defensive distinction in this paper under the formulation of multi-label classification, for protecting against adversarial examples.   We design and perform experiments using the MNIST dataset to investigate these two questions, and obtain highly positive results demonstrating the strong distinguishability of adversarial examples.   We recommend that this unique defensive distinction approach should be seriously considered to complement other defense approaches.
answer:

We propose a defensive distinction protection approach and demonstrate the strong distinguishability of adversarial examples.