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.
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Question: Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans.   Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs.   Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network~(GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.


Answer: Jointly train an adversarial noise generating network with a classification network to provide better robustness to adversarial attacks.


Question: Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score.

Answer: An end-to-end trained deep neural network that leverages Gaussian Mixture Modeling to perform density estimation and unsupervised anomaly detection in a low-dimensional space learned by deep autoencoder.


Question: Exploration while learning representations is one of the main challenges Deep
 Reinforcement Learning (DRL) faces today. As the learned representation is dependant in the observed data, the exploration strategy has a crucial role. The popular DQN algorithm has improved significantly the capabilities of Reinforcement
 Learning (RL) algorithms to learn state representations from raw data, yet, it uses
 a naive exploration strategy which is statistically inefficient. The Randomized
 Least Squares Value Iteration (RLSVI) algorithm (Osband et al., 2016), on the
 other hand, explores and generalizes efficiently via linearly parameterized value
 functions. However, it is based on hand-designed state representation that requires
 prior engineering work for every environment. In this paper, we propose a Deep
 Learning adaptation for RLSVI. Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by a
 DQN agent. As the representation is being optimized during the learning process,
 a key component for the suggested method is a likelihood matching mechanism,
 which adapts to the changing representations. We demonstrate the importance of
 the various properties of our algorithm on a toy problem and show that our method
 outperforms DQN in five Atari benchmarks, reaching competitive results with the
 Rainbow algorithm.

Answer:
A Deep Learning adaptation of Randomized Least Squares Value Iteration