**Dropping Network**

In natural language understanding, many challenges require learning relationships between two sequences for various tasks such as similarity, relatedness, paraphrasing and question matching. Some of these challenges are inherently closer in nature, hence the knowledge acquired from one task to another is easier acquired and adapted. However, transferring all knowledge might be undesired and can lead to sub-optimal results due to \textit{negative} transfer. Hence, this paper focuses on the transferability of both instances and parameters across natural language understanding tasks using an ensemble-based transfer learning method to circumvent such issues. The primary contribution of this paper is the combination of both \textit{Dropout} and \textit{Bagging} for improved transferability in neural networks, referred to as \textit{Dropping} herein. Secondly, we present a straightforward yet novel approach to incorporating source \textit{Dropping} Networks to a target task for few-shot learning that mitigates \textit{negative} transfer. This is achieved by using a decaying parameter chosen according to the slope changes of a smoothed spline error curve at sub-intervals during training. We compare the approach over the hard parameter sharing, soft parameter sharing and single-task learning to compare its effectiveness. The aforementioned adjustment leads to improved transfer learning performance and comparable results to the current state of the art only using few instances from the target task. … **Gretel**

We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the interaction of an agent with a network—a driver on the transportation network, an information seeker in Wikipedia, or a client in an online shop. Our interest is sparked by the realization that, in contrast to shortest-path problems, natural paths are usually not optimal in any graph-theoretic sense, but might still follow predictable patterns. Our main contribution is a graph neural network called Gretel. Conditioned on a path prefix, this network can efficiently extrapolate path suffixes, evaluate path likelihood, and sample from the future path distribution. Our experiments with GPS traces on a road network and user-navigation paths in Wikipedia confirm that Gretel is able to adapt to graphs with very different properties, while also comparing favorably to previous solutions. … **Atrain Distributed System (ADS)**

A special type of distributed system called by ‘Atrain Distributed System’ (ADS) which is very suitable for processing big data using the heterogeneous data structures r-atrain or the homogeneous data structure r-train. A simple ‘Atrain Distributed System’ is called an uni-tier ADS. The ‘Multi-tier Atrain Distributed System’ is an extension of the uni-tier ADS. The ADS is scalable upto any extent as many times as required. Two new type of network topologies are defined for ADS called by ‘multi-horse cart’ topology and ‘cycle’ topology which can support increasing volume of big data. Where r-atrain and r-train data structures are introduced for the processing of big data, the data structures ‘heterogeneous data structure MA’ and ‘homogeneous data structure MT’ are introduced for the processing of big data including temporal big data too. Both MA and MT can be well implemented in multi-tier ADS. We define cyclic train and cyclic atrain, and then doubly linked train/atrain. A method is proposed on how to implement Solid Matrices, n-dimensional arrays, n-dimensional larrays etc. in a computer memory using the data structures MT and MA.

http://…/biswasIJCO1-4-2014-2.pdf … **Graph Markov Neural Network (GMNN)**

This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results. …

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