instruction:
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.
question:
In this paper, we propose a differentiable adversarial grammar model for future prediction. The objective is to model a formal grammar in terms of differentiable functions and latent representations, so that their learning is possible through standard backpropagation. Learning a formal grammar represented with latent terminals, non-terminals, and productions rules allows capturing sequential structures with multiple possibilities from data.

 The adversarial grammar is designed so that it can learn stochastic production rules from the data distribution. Being able to select multiple production rules leads to different predicted outcomes, thus efficiently modeling many plausible futures.   We confirm the benefit of the adversarial grammar on two diverse tasks: future 3D human pose prediction and future activity prediction. For all settings, the proposed adversarial grammar outperforms the state-of-the-art approaches, being able to predict much more accurately and further in the future, than prior work.
answer:
We design a grammar that is learned in an adversarial setting and apply it to future prediction in video.


question:
Effective training of neural networks requires much data. In the low-data regime,
 parameters are underdetermined, and learnt networks generalise poorly. Data
 Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data
 more effectively. However standard data augmentation produces only limited
 plausible alternative data. Given there is potential to generate a much broader set
 of augmentations, we design and train a generative model to do data augmentation.
 The model, based on image conditional Generative Adversarial Networks, takes
 data from a source domain and learns to take any data item and generalise it
 to generate other within-class data items. As this generative process does not
 depend on the classes themselves, it can be applied to novel unseen classes of data.
 We show that a Data Augmentation Generative Adversarial Network (DAGAN)
 augments standard vanilla classifiers well. We also show a DAGAN can enhance
 few-shot learning systems such as Matching Networks. We demonstrate these
 approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and
 VGG-Face data. In our experiments we can see over 13% increase in accuracy in
 the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%
 to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we
 observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in
 EMNIST (from 59.5% to 61.3%).
answer:
Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distance


question:
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications. Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models. For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.
answer:

We compress and speed up speech recognition models on embedded devices through a trace norm regularization technique and optimized kernels.