Advanced deep learning models

This research paper focuses on the critical role of demand forecasting in FMCG, emphasizing the need for LSTM-based deep learning models to deal with demand uncertainty and improve predictive outcomes. Through this exploration, we aim to illuminate the link between demand forecasting and advanced deep learning, enabling FMCG companies to thrive in a highly dynamic business landscape.

8p vifilm 11-10-2024 10 1   Download

The resulting model is a Document Retriever, called QASA, which is then integrated with a machine reader to form a complete open-domain QA system. Our system is thoroughly evaluated using QUASAR-T dataset and shows surpassing results compared to other state-of-the-art methods.

67p tamynhan1 13-06-2020 22 4   Download

Furthermore, experiments on the task of RE proved that data representation is one of the most influential factors to the model’s performance but still has many limitations. We propose a compositional embedding that combines several dominant linguistic as well as architectural features and dependency tree normalization techniques for generating rich representations for both words and dependency relations in the SDP

82p tamynhan1 13-06-2020 20 3   Download