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: This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit. Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion. This can be interpreted as a form of meta-gating and recursively parameterizing a recurrent model. We postulate that our proposed inductive bias provides modeling benefits pertaining to learning with inherently hierarchically-structured sequence data (e.g., language, logical or music tasks). To this end, we conduct extensive experiments on recursive logic tasks (sorting, tree traversal, logical inference), sequential pixel-by-pixel classification, semantic parsing, code generation, machine translation and polyphonic music modeling, demonstrating the widespread utility of the proposed approach, i.e., achieving state-of-the-art (or close) performance on all tasks.

SOLUTION: Recursive Parameterization of Recurrent Models improve performance

PROBLEM: Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data. At the same time, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper we propose a model, Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model achieves state-of-the-art performance on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.

SOLUTION: We make a network of Graph Convolution Networks, feeding each a different power of the adjacency matrix, combining all their representation into a classification sub-network, achieving state-of-the-art on semi-supervised node classification.

PROBLEM: As machine learning becomes ubiquitous, deployed systems need to be as accu- rate as they can. As a result, machine learning service providers have a surging need for useful, additional training data that benefits training, without giving up all the details about the trained program. At the same time, data owners would like to trade their data for its value, without having to first give away the data itself be- fore receiving compensation. It is difficult for data providers and model providers to agree on a fair price without first revealing the data or the trained model to the other side. Escrow systems only complicate this further, adding an additional layer of trust required of both parties. Currently, data owners and model owners don't have a fair pricing system that eliminates the need to trust a third party and training the model on the data, which 1) takes a long time to complete, 2) does not guarantee that useful data is paid valuably and that useless data isn't, without trusting in the third party with both the model and the data. Existing improve- ments to secure the transaction focus heavily on encrypting or approximating the data, such as training on encrypted data, and variants of federated learning. As powerful as the methods appear to be, we show them to be impractical in our use case with real world assumptions for preserving privacy for the data owners when facing black-box models. Thus, a fair pricing scheme that does not rely on secure data encryption and obfuscation is needed before the exchange of data. This pa- per proposes a novel method for fair pricing using data-model efficacy techniques such as influence functions, model extraction, and model compression methods, thus enabling secure data transactions. We successfully show that without running the data through the model, one can approximate the value of the data; that is, if the data turns out redundant, the pricing is minimal, and if the data leads to proper improvement, its value is properly assessed, without placing strong assumptions on the nature of the model. Future work will be focused on establishing a system with stronger transactional security against adversarial attacks that will reveal details about the model or the data to the other party.

SOLUTION:
Facing complex, black-box models, encrypting the data is not as usable as approximating the model and using it to price a potential transaction.