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: 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.

Answer: GANs are shown to provide us a new effective robust mean estimate against agnostic contaminations with both statistical optimality and practical tractability.


Question: The physical design of a robot and the policy that controls its motion are inherently coupled. However, existing approaches largely ignore this coupling, instead choosing to alternate between separate design and control phases, which requires expert intuition throughout and risks convergence to suboptimal designs. In this work, we propose a method that jointly optimizes over the physical design of a robot and the corresponding control policy in a model-free fashion, without any need for expert supervision. Given an arbitrary robot morphology, our method maintains a distribution over the design parameters and uses reinforcement learning to train a neural network controller. Throughout training, we refine the robot distribution to maximize the expected reward. This results in an assignment to the robot parameters and neural network policy that are jointly optimal. We evaluate our approach in the context of legged locomotion, and demonstrate that it discovers novel robot designs and walking gaits for several different morphologies, achieving performance comparable to or better than that of hand-crafted designs.

Answer: Use deep reinforcement learning to design the physical attributes of a robot jointly with a control policy.


Question: Transformers have achieved state-of-the-art results on a variety of natural language processing tasks. 
 Despite good performance, Transformers are still weak in long sentence modeling where the global attention map is too dispersed to capture valuable information.
 In such case, the local/token features that are also significant to sequence modeling are omitted to some extent.
 To address this problem, we propose a Multi-scale attention model (MUSE) by concatenating attention networks with convolutional networks and position-wise feed-forward networks to explicitly capture local and token features. Considering the parameter size and computation efficiency, we re-use the feed-forward layer in the original Transformer and adopt a lightweight dynamic convolution as implementation. 
 Experimental results show that the proposed model achieves substantial performance improvements over Transformer, especially on long sentences, and pushes the state-of-the-art from 35.6 to 36.2 on IWSLT 2014  German to English translation task,  from 30.6 to 31.3 on  IWSLT 2015 English to Vietnamese translation task. We also reach the state-of-art performance on  WMT 2014 English to French translation dataset, with a BLEU score of 43.2.

Answer:
This paper propose a new model  which combines multi scale information for sequence to sequence learning.