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.

[EX Q]: Modeling informal inference in natural language is very challenging. With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI from the data? If not, how can NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we aim to answer these questions by enriching the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models with external knowledge further improve the state of the art on the Stanford Natural Language Inference (SNLI) dataset.
[EX A]: the proposed models with external knowledge further improve the state of the art on the SNLI dataset.

[EX Q]: 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.
[EX A]: Use deep reinforcement learning to design the physical attributes of a robot jointly with a control policy.

[EX Q]: Nowadays deep learning is one of the main topics in almost every field. It helped to get amazing results in a great number of tasks. The main problem is that this kind of learning and consequently neural networks, that can be defined deep, are resource intensive. They need specialized hardware to perform a computation in a reasonable time. Unfortunately, it is not sufficient to make deep learning "usable" in real life. Many tasks are mandatory to be as much as possible real-time. So it is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them "efficient and usable". All these optimizations can help us to produce incredibly accurate and fast learning models.
[EX A]:
Embedded architecture for deep learning on optimized devices for face detection and emotion recognition