TASK DEFINITION: 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.
PROBLEM: 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.

SOLUTION: 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.

PROBLEM: Understanding the groundbreaking performance of Deep Neural Networks is one
of the greatest challenges to the scientific community today. In this work, we
introduce an information theoretic viewpoint on the behavior of deep networks
optimization processes and their generalization abilities. By studying the Information
Plane, the plane of the mutual information between the input variable and
the desired label, for each hidden layer. Specifically, we show that the training of
the network is characterized by a rapid increase in the mutual information (MI)
between the layers and the target label, followed by a longer decrease in the MI
between the layers and the input variable. Further, we explicitly show that these
two fundamental information-theoretic quantities correspond to the generalization
error of the network, as a result of introducing a new generalization bound that is
exponential in the representation compression. The analysis focuses on typical
patterns of large-scale problems. For this purpose, we introduce a novel analytic
bound on the mutual information between consecutive layers in the network.
An important consequence of our analysis is a super-linear boost in training time
with the number of non-degenerate hidden layers, demonstrating the computational
benefit of the hidden layers.

SOLUTION: Introduce an information theoretic viewpoint on the behavior of deep networks optimization processes and their generalization abilities

PROBLEM: Visual grounding of language is an active research field aiming at enriching text-based representations with visual information. In this paper, we propose a new way to leverage visual knowledge for sentence representations. Our approach transfers the structure of a visual representation space to the textual space by using two complementary sources of information: (1) the cluster information: the implicit knowledge that two sentences associated with the same visual content describe the same underlying reality and (2) the perceptual information contained within the structure of the visual space. We use a joint approach to encourage beneficial interactions during training between textual, perceptual, and cluster information. We demonstrate the quality of the learned representations on semantic relatedness, classification, and cross-modal retrieval tasks.

SOLUTION:
We propose a joint model to incorporate visual knowledge in sentence representations