Efficient Evolution of Neural Architecture (EENA)
Latest algorithms for automatic neural architecture search perform remarkable but basically directionless in search space and computational expensive in the training of every intermediate architecture. In this paper, we propose a method for efficient architecture search called EENA (Efficient Evolution of Neural Architecture) with mutation and crossover operations guided by the information have already been learned to speed up this process and consume less computational effort by reducing redundant searching and training. On CIFAR-10 classification, EENA using minimal computational resources (0.65 GPU-days) can design highly effective neural architecture which achieves 2.56% test error with 8.47M parameters. Furthermore, The best architecture discovered is also transferable for CIFAR-100. …

Task Transfer Net (TTNet)
In this work, we present a novel meta-learning algorithm, i.e. TTNet (Task Transfer Net), that regresses model parameters for novel tasks for which no ground truth is available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our meta-learner learns from the model parameters of known tasks (with ground truth) and the correlation of known tasks to zero-shot tasks. Such intuition finds its foothold in cognitive science, where a subject (human baby) can adapt to a novel-concept (depth understanding) by correlating it with old concepts (hand movement or self-motion), without receiving explicit supervision. We evaluated our model on the Taskonomy dataset, with four tasks as zero-shot: surface-normal, room layout, depth, and camera pose estimation. These tasks were chosen based on the data acquisition complexity and the complexity associated with the learning process using a deep network. Our proposed methodology out-performs state-of-the-art models (which use ground truth)on each of our zero-shot tasks, showing promise on zero-shot task transfer. We also conducted extensive experiments to study the various choices of our methodology, as well as showed how the proposed method can also be used in transfer learning. To the best of our knowledge, this is the firstsuch effort on zero-shot learning in the task space. …

Data Augmentation
Data augmentation adds value to base data by adding information derived from internal and external sources within an enterprise. Data is one of the core assets for an enterprise, making data management essential. Data augmentation can be applied to any form of data, but may be especially useful for customer data, sales patterns, product sales, where additional information can help provide more in-depth insight. Data augmentation can help reduce the manual interventation required to developed meaningful information and insight of business data, as well as significantly enhance data quality. Data augmentation is of the last steps done in enterprise data management after monitoring, profiling and integration Some of the common techniques used in data augmentation include:
· Extrapolation Technique: Based on heuristics. The relevant fields are updated or provided with values.
· Tagging Technique: Common records are tagged to a group, making it easier to understand and differentiate for the group.
· Aggregation Technique: Using mathematical values of averages and means, values are estimated for relevant fields if needed
· Probability Technique: Based on heuristics and analytical statistics, values are populated based on the probability of events.
https://…/01-jcgs-art.pdf …

Knowledge Extraction
Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. …