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.

Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining. Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest. In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available. Our main contributions are as follows. First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier. Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student). Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words. Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.
We effectively leverage a few keywords as weak supervision for training neural networks for aspect extraction.

In this work, we focus on the problem of grounding language by training an agent
to follow a set of natural language instructions and navigate to a target object
in a 2D grid environment. The agent receives visual information through raw
pixels and a natural language instruction telling what task needs to be achieved.
Other than these two sources of information, our model does not have any prior
information of both the visual and textual modalities and is end-to-end trainable.
We develop an attention mechanism for multi-modal fusion of visual and textual
modalities that allows the agent to learn to complete the navigation tasks and also
achieve language grounding. Our experimental results show that our attention
mechanism outperforms the existing multi-modal fusion mechanisms proposed in
order to solve the above mentioned navigation task. We demonstrate through the
visualization of attention weights that our model learns to correlate attributes of
the object referred in the instruction with visual representations and also show
that the learnt textual representations are semantically meaningful as they follow
vector arithmetic and are also consistent enough to induce translation between instructions
in different natural languages. We also show that our model generalizes
effectively to unseen scenarios and exhibit zero-shot generalization capabilities.
In order to simulate the above described challenges, we introduce a new 2D environment
for an agent to jointly learn visual and textual modalities
Attention based architecture for language grounding via reinforcement learning in a new customizable 2D grid environment

This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier. Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images. The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 60% of strong gray-box and 90% of strong black-box attacks by a variety of major attack methods.
We apply a model-agnostic defense strategy against adversarial examples and achieve 60% white-box accuracy and 90% black-box accuracy against major attack algorithms.