Year of Graduation
Interpretable Machine Learning Models
Today machine learning methods are used in many areas: from natural language processing and face recognition to self-driving cars. But models can be difficult to interpretate and there are cases when it is necessary to understand why the algorithm provides certain output. This work consists of research of gradient interpretable methods using adversarial attacks.