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: We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference (i.e., thematic fit) and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%. The proposed framework is versatile and holds promise to support learning function-specific representations beyond the SVO structures.

SOLUTION: Task-independent neural model for learning associations between interrelated groups of words.

PROBLEM: This paper introduces a framework for solving combinatorial optimization problems by learning from input-output examples of optimization problems. We introduce a new memory augmented neural model in which the memory is not resettable (i.e the information stored in the memory after processing an input example is kept for the next seen examples). We used deep reinforcement learning to train a memory controller agent to store useful memories. Our model was able to outperform hand-crafted solver on Binary Linear Programming (Binary LP). The proposed model is tested on different Binary LP instances with large number of variables (up to 1000 variables) and constrains (up to 700 constrains).

SOLUTION: We propose a memory network model to solve Binary LP instances where the memory information is perseved for long-term use. 

PROBLEM: Deep learning has become a widely used tool in many computational and classification problems. 
 Nevertheless obtaining and labeling data, which is needed for strong results, is often expensive or even not possible. 
 In this paper three different algorithmic approaches to deal with limited access to data are evaluated and compared to each other. 
 We show the drawbacks and benefits of each method. 
 One successful approach, especially in one- or few-shot learning tasks, is the use of external data during the classification task. 
 Another  successful approach, which achieves state of the art results in semi-supervised learning (SSL) benchmarks, is consistency regularization.
 Especially virtual adversarial training (VAT) has shown strong results and will be investigated in this paper. 
 The aim of consistency regularization is to force the network not to change the output, when the input or the network itself is perturbed.
 Generative adversarial networks (GANs) have also shown strong empirical results. 
 In many approaches the GAN architecture is used in order to create additional data and therefor to increase the generalization capability of the classification network.
 Furthermore we consider the use of unlabeled data for further performance improvement. 
 The use of unlabeled data is investigated both for GANs and VAT. 


SOLUTION:
Comparison of siamese neural networks, GANs, and VAT for few shot learning.