Application of Deep Neural Networks (DNN) for Modern Data with two examples; Recommender Systems and User Recognition
For almost two decades, we have been dealing with “Modern Data” as the prevalent type of data in many areas of science and technology. “Modern Data” has unique characteristics such as, extreme sparsity, very high correlation, massive size and high dimensionality. A major difficulty is that many of the old machine learning models cannot be applied on Modern Data. In this talk, we show our deploying DNN models in dealing with modern data effectively as , among other advantages, DNN models do not rely on many of the assumptions and abstractions the traditional models depend on.
Kourosh has completed his graduate studies at Stanford (MS, PhD). His PhD dissertation, “A local regularization method using multiple regularization levels“, focused on developing new modeling approaches for a major issue in AI and Machine Learning, regularization. His PhD advisor was Gene Golub. He has been working as entrepreneur and also as Sr AI- Machine Learning scientist at Adobe where he has been the founder and manager of Adobe AI-Machine Learning group. At Adobe, Kourosh has been leading many projects such as "AI-based Recommender Systems", “Automated, Insightful and Interpretable Clustering” and "AI-based User Comprehensive View – Connection of users cross devices, venues and channels".