석준희 연구실

Deep learning has been applied in a variety of areas including finance, medicine, image and speech recognition. For artificial neural networks, reliable factor analysis can increase confidence in prediction results, especially in the medical, financial, aviation, and defense sectors where a user requires confidence in results. We perform a study for deep learning approaches focusing on interpretability. Usually, a deep learning model predicts a certain value, such as probability or portfolio return. However, reinforce learning does not correspond to learning a value; instead, it corresponds to learning a policy. The reinforce learning focuses on how to obtain maximum rewards given an environment. We are applying such reinforce learning techniques for finance domain in particular. Weather forecast has been conventionally conducted only by meteorological methods which bring difficulties to analyze effects of complex factors. In contrast, deep learning models have an advantage to make such problems solvable by considering inherent dependencies among variables. Therefore, the deep learning can show a possibility to improve the performance for weather forecast; in addition, we can easily extend it to a further research, such as weather-dependent energy demand forecasting. We intend to innovate the design of nanophotonic material devices using novel AI technology by learning intuition and experience required for the design. By employing the finite-difference frequency-domain (FDFD) method, we built a database for the learning of AI models. With the database and the AI models, we can predict the characteristics of a certain nanophotonic device and automatically design a nanophotonic device with the desired characteristics. We use deep learning techniques to learn financial dat