18,484 research outputs found
NASARI: a novel approach to a Semantically-Aware Representation of items
The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage
We propose a novel method to merge convolutional neural-nets for the
inference stage. Given two well-trained networks that may have different
architectures that handle different tasks, our method aligns the layers of the
original networks and merges them into a unified model by sharing the
representative codes of weights. The shared weights are further re-trained to
fine-tune the performance of the merged model. The proposed method effectively
produces a compact model that may run original tasks simultaneously on
resource-limited devices. As it preserves the general architectures and
leverages the co-used weights of well-trained networks, a substantial training
overhead can be reduced to shorten the system development time. Experimental
results demonstrate a satisfactory performance and validate the effectiveness
of the method.Comment: To appear in the 27th International Joint Conference on Artificial
Intelligence and the 23rd European Conference on Artificial Intelligence,
2018. (IJCAI-ECAI 2018
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