Titlebook: Logical Foundations for Cognitive Agents; Contributions in Hon Hector J. Levesque,Fiora Pirri Book 1999 Springer-Verlag Berlin Heidelberg 1

fatty-acids

Fahiem Bacchusummary, which will be used as the guidance indicator. We design a summary-aware review encoder to learn representations of reviews from raw words, and another summary-aware user/item encoder to learn representations of users or items from reviews. To be specific, we propose a hierarchical attention

規(guī)章

JOG

Craig Boutilier,Moisés Goldszmidtal similarity measure is learned by distance metric learning. Experimental results show that, by leveraging the rich relational semantics in texts, our model can outperform the state-of-the-art models by 3.4% on 6.3% in accuracy on two benchmark datasets.

要塞

John McCarthyerm and short-term, modeled by LSTM and Attention-based model respectively for user’s next click recommendation. We refer this model as LANCR and analyze the model in experiment. The experiment demonstrates that the proposed model has superior improvement compared with standard approaches. We deploy

urethritis

Giovanni Criscuolo,Eliana Minicozziwithin the knowledge graph, ultimately enhancing the model’s effectiveness. At the same time, we add a self-attention mechanism to trim the action space, which solves the problem of large action space of knowledge graph and improves the effectiveness and efficiency of agent action selection. We perf

詩集

租約

偽善

Marc Denecker,V. Wiktor Marek,Miros?aw Truszczyńskiyle of the source domain. Meanwhile, SimPGAN uses the similarity consistency loss, which is measured by a siamese deep convolutional neural network, to preserve the similarity of the transformed images of the same person. Comprehensive experiments based on multiple real surveillance datasets are con

aggrieve

CLAY