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]: Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring both training and inference processes to low-bitwidth integers has not been demonstrated simultaneously. In this work, we develop a new method termed as ``"WAGE" to discretize both training and inference, where weights (W), activations (A), gradients (G) and errors (E) among layers are shifted and linearly constrained to low-bitwidth integers. To perform pure discrete dataflow for fixed-point devices, we further replace batch normalization by a constant scaling layer and simplify other components that are arduous for integer implementation. Improved accuracies can be obtained on multiple datasets, which indicates that WAGE somehow acts as a type of regularization. Empirically, we demonstrate the potential to deploy training in hardware systems such as integer-based deep learning accelerators and neuromorphic chips with comparable accuracy and higher energy efficiency, which is crucial to future AI applications in variable scenarios with transfer and continual learning demands.
[EX A]: We apply training and inference with only low-bitwidth integers in DNNs

[EX Q]: Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.
[EX A]: We propose an algorithm that automatically adjusts parameters of a simulation engine to generate training data for a neural network such that validation accuracy is maximized.

[EX Q]: Deep learning (DL) has in recent years been widely used in natural
 language processing (NLP) applications due to its superior
 performance. However, while natural languages are rich in
 grammatical structure, DL has not been able to explicitly
 represent and enforce such structures. This paper proposes a new
 architecture to bridge this gap by exploiting tensor product
 representations (TPR), a structured neural-symbolic framework
 developed in cognitive science over the past 20 years, with the
 aim of integrating DL with explicit language structures and rules.
 We call it the Tensor Product Generation Network
 (TPGN), and apply it to image captioning. The key
 ideas of TPGN are: 1) unsupervised learning of
role-unbinding vectors of words via a TPR-based deep neural
network, and 2) integration of TPR with typical DL architectures
 including Long Short-Term Memory (LSTM) models. The novelty of our
 approach lies in its ability to generate a sentence and extract
 partial grammatical structure of the sentence by using
 role-unbinding vectors, which are obtained in an unsupervised
 manner. Experimental results demonstrate the effectiveness of the
 proposed approach.
[EX A]:
This paper is intended to develop a tensor product representation approach for deep-learning-based natural language processinig applications.