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.

[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.
[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.


[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.
[A]: Embedded architecture for deep learning on optimized devices for face detection and emotion recognition 


[Q]: Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences.   While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult.    In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete.   Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models.   The EBM then works
in the {\em residual} of the language model; and is trained to discriminate real text from text generated by the auto-regressive models.
 We  investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications.   We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives. Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives. However, EBMs exhibit more sensitivity to the training set used by such generators.
[A]:
A residual EBM for text whose formulation is equivalent to discriminating between human and machine generated text. We study its generalization behavior.