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.

Input: Consider Input: 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.

Output: the proposed models with external knowledge further improve the state of the art on the SNLI dataset.


Input: Consider Input: This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees. We empirically verify this and show that this does not hold when the initial conditions are chosen at random. The proof of convergence investigates the interaction between the two layers of the network. Our results highlight the importance of using symmetry in the design of neural networks.

Output: When initialized properly, neural networks can learn the simple class of symmetric functions; when initialized randomly, they fail.  


Input: Consider Input: The problem of visual metamerism is defined as finding a family of perceptually
 indistinguishable, yet physically different images. In this paper, we propose our
 NeuroFovea metamer model, a foveated generative model that is based on a mixture
 of peripheral representations and style transfer forward-pass algorithms. Our
 gradient-descent free model is parametrized by a foveated VGG19 encoder-decoder
 which allows us to encode images in high dimensional space and interpolate
 between the content and texture information with adaptive instance normalization
 anywhere in the visual field. Our contributions include: 1) A framework for
computing metamers that resembles a noisy communication system via a foveated
feed-forward encoder-decoder network – We observe that metamerism arises as a
byproduct of noisy perturbations that partially lie in the perceptual null space; 2)
A perceptual optimization scheme as a solution to the hyperparametric nature of
our metamer model that requires tuning of the image-texture tradeoff coefficients
everywhere in the visual field which are a consequence of internal noise; 3) An
 ABX psychophysical evaluation of our metamers where we also find that the rate
 of growth of the receptive fields in our model match V1 for reference metamers
 and V2 between synthesized samples. Our model also renders metamers at roughly
 a second, presenting a ×1000 speed-up compared to the previous work, which now
 allows for tractable data-driven metamer experiments.


Output: We introduce a novel feed-forward framework to generate visual metamers