193,921 research outputs found

    ProThes: Thesaurus-based Meta-Search Engine for a Specific Application Domain

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    In this poster we introduce ProThes, a pilot meta-search engine (MSE) for a specific application domain. ProThes combines three approaches: meta-search, graphical user interface (GUI) for query specification, and thesaurus-based query techniques. ProThes attempts to employ domain-specific knowledge, which is represented by both a conceptual thesaurus and results ranking heuristics. Since the knowledge representation is separated from the MSE core, adjusting the system to a specific domain is trouble free. Thesaurus allows for manual query building and automatic query techniques. This poster outlines the overall system architecture, thesaurus representation format, and query operations. ProThes is implemented on J2EE platform as a Web service.The project was supported in part by the Russian Fund of Basic Research, grant # 03-07-90342

    Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks

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    Heterogeneous information networks (HINs) are widely employed for describing real-world data with intricate entities and relationships. To automatically utilize their semantic information, graph neural architecture search has recently been developed on various tasks of HINs. Existing works, on the other hand, show weaknesses in instability and inflexibility. To address these issues, we propose a novel method called Partial Message Meta Multigraph search (PMMM) to automatically optimize the neural architecture design on HINs. Specifically, to learn how graph neural networks (GNNs) propagate messages along various types of edges, PMMM adopts an efficient differentiable framework to search for a meaningful meta multigraph, which can capture more flexible and complex semantic relations than a meta graph. The differentiable search typically suffers from performance instability, so we further propose a stable algorithm called partial message search to ensure that the searched meta multigraph consistently surpasses the manually designed meta-structures, i.e., meta-paths. Extensive experiments on six benchmark datasets over two representative tasks, including node classification and recommendation, demonstrate the effectiveness of the proposed method. Our approach outperforms the state-of-the-art heterogeneous GNNs, finds out meaningful meta multigraphs, and is significantly more stable.Comment: 12 pages, 7 figures, 8 tables, accepted by AAAI 2023 conferenc

    Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

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    Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning, which is guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020
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