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.

In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq)  model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to  dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training.
This paper aims to provide an empirical answer to the question of whether well-trained dialogue response model can output malicious responses.

One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by the normal aging process. Aging is therefore a confounding factor, whose effects have been hard for machine learning classifiers to isolate. 

 In this paper, we show that deep neural network (DNN) classifiers can infer ages from linguistic features, which is an entanglement that could lead to unfairness across age groups. We show this problem is caused by undesired activations of v-structures in causality diagrams, and it could be addressed with fair representation learning. We build neural network classifiers that learn low-dimensional representations reflecting the impacts of dementia yet discarding the effects of age. To evaluate these classifiers, we specify a model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier results are disentangled from age. Our best models outperform baseline neural network classifiers in disentanglement, while compromising accuracy by as little as 2.56\% and 2.25\% on DementiaBank and the Famous People dataset respectively.
Show that age confounds cognitive impairment detection + solve with fair representation learning + propose metrics and models.

Deep learning has made remarkable achievement in many fields. However, learning
 the parameters of neural networks usually demands a large amount of labeled
 data. The algorithms of deep learning, therefore, encounter difficulties when applied
 to supervised learning where only little data are available. This specific task
 is called few-shot learning. To address it, we propose a novel algorithm for fewshot
 learning using discrete geometry, in the sense that the samples in a class are
 modeled as a reduced simplex. The volume of the simplex is used for the measurement
 of class scatter. During testing, combined with the test sample and the
 points in the class, a new simplex is formed. Then the similarity between the test
 sample and the class can be quantized with the ratio of volumes of the new simplex
 to the original class simplex. Moreover, we present an approach to constructing
 simplices using local regions of feature maps yielded by convolutional neural networks.
 Experiments on Omniglot and miniImageNet verify the effectiveness of
 our simplex algorithm on few-shot learning.

A simplex-based geometric method is proposed to cope with few-shot learning problems.