AIC Lab Seminar: "Utilizing empirical p-values in False Discovery Rate control and examination of the reasoning capacity of the deep net based METDR method".
On Friday, May 26 at 12:00 pm, during the seminar on the topic: "Utilizing empirical p-values in False Discovery Rate control and examination of the reasoning capacity of the deep net based METDR method", two reports will be presented.
Artificial Intelligence has been demonstrated as an incredibly useful instrument for a broad range of tasks. One of them — Bioinformatics — proposed fundamentally novel techniques at the intersection of machine learning and statistics. Despite their potential, these methods have not been utilized for more general tasks yet. Accordingly, in our work, we elaborate a drastically new approach called empirical p-values (EPV). Assuming negative training data of classification task to be the null hypothesis distribution, we calculate the corresponding p-values for test samples. Later, we expand the BH procedure to control FDR, making it possible both to regulate the interrelation of train and test data distributions, as well as to predict new labels based on those already investigated. The major goal is to accurately predict number of accepted discoveries at each level without true labels.
2nd year Master's student on Data Science
Visual Question Answering (VQA) is an important task in Artificial Intelligence which utilizes a combination of visual and textual data to answer questions. Recent advancements in deep learning, including transformer-based models, have allowed VQA systems to achieve results that are comparable to those of humans. However, questions remain regarding their ability to accurately reason and understand real-world concepts and relationships. Progress in the VQA task was helped by the implementation of large-scale datasets which often necessitated the use of complex visual and textual reasoning. A central challenge for deep learning models is the lack of reliable metrics when evaluating their reasoning abilities. To investigate the “Clever Hans” phenomenon, we have designed a new dataset which uses limited visual and textual concepts but requires complex reasoning skills and understanding. This study provides a methodology for building such datasets, along with a description of Blender and Python scripts and tasks. We also present the performance of pre-trained MDETR model on our dataset.