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.

Q: 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.

A: This paper aims to provide an empirical answer to the question of whether well-trained dialogue response model can output malicious responses.
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Q: High performance of deep learning models typically comes at cost of considerable model size and computation time. These factors limit applicability for deployment on memory and battery constraint devices such as mobile phones or embedded systems. In this work we propose a novel pruning technique that eliminates entire filters and neurons according to their relative L1-norm as compared to the rest of the network, yielding more compression and decreased redundancy in the parameters. The resulting network is non-sparse, however, much more compact and requires no special infrastructure for its deployment. We prove the viability of our method by achieving 97.4%, 47.8% and 53% compression of LeNet-5, ResNet-56 and ResNet-110 respectively, exceeding state-of-the-art compression results reported on ResNet without losing any performance compared to the baseline. Our approach does not only exhibit good performance, but is also easy to implement on many architectures.

A: We propose a novel structured class-blind pruning technique to produce highly compressed neural networks.
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Q: We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.   We propose a methodology for testing for statistical differences in model performances under several replications. To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM. We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time. We find that the selected implementations exhibit stability across random seed, and repeats.

A:
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
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