193,921 research outputs found
ProThes: Thesaurus-based Meta-Search Engine for a Specific Application Domain
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
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
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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