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.

Example Input: Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science. Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability. In this paper, we establish an intriguing connection between f-GANs and various depth functions through the lens of f-Learning. Similar to the derivation of f-GAN, we show that these depth functions that lead to rate-optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of f-Learning. This connection opens the door of computing robust estimators using tools developed for training GANs. In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model. Interestingly, the hidden layers of the neural net structure in the discriminator class are shown to be necessary for robust estimation.
Example Output: GANs are shown to provide us a new effective robust mean estimate against agnostic contaminations with both statistical optimality and practical tractability.

Example Input: 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.
Example Output: We propose a novel structured class-blind pruning technique to produce highly compressed neural networks.

Example Input: This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive
 models for raw audio waveform generation. As an example, we propose
 a hybrid model that combines an autoregressive network named WaveNet and a
 conventional LSTM model to address speech synthesis. Instead of generating
 one sample per time-step, the proposed HybridNet generates multiple samples per
 time-step by exploiting the long-term memory utilization property of LSTMs. In
 the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.
 HybridNet achieves a 3.83 subjective 5-scale mean opinion score on
 US English, largely outperforming the same size WaveNet in terms of naturalness
 and provide 2x speed up at inference.
Example Output:
It is a hybrid neural architecture to speed-up autoregressive model.