Kas 2019
BBL 596E Seminerleri

BBL 596E kapsamında gerçekleştirilecek olan seminerlerin listesi aşağıda verilmiştir. Sunumlar 409 Nolu sınıfta yapılacaktır.

Öğrenci No: 704181008
Ad Soyad: Esra Ergün
Başlık: Feature Diversification on Sparse Progressive Neural Networks for Continual Learning
Özet/Abstract: Human brain effectively integrate prior knowledge with new skills by transferring experience across tasks without suffering from catastrophic forgetting. Performance of state-of-the-art neural networks are impressive on computer vision and natural language processing and they outperm humans on number of tasks. However, they perform modest on knowledge transfer and continual learning. The goal of this work is to address memory overhead of existing methods and to improve forward transfer through tasks with feature diversification. This study combines sparsity and additional loss terms for feature diversification on progressive neural networks [0] to continually learn multiple tasks. This approach will be evaluated on permuted MNIST, class MNIST, and class CIFAR10 datasets.
Danışman Ad Soyad:  Behçet Uğur Töreyin
Sunum Tarihi: 26.11.2019
Sunum Saati: 09.30

Öğrenci No: 704181010
Ad Soyad: Gözde Filiz
Başlık: Reordering Buffer Management Problem
Özet/Abstract: In the Reordering Buffer Management (RBM) Problem, we are given a service station and a random-access buffer with a limited capacity. An input sequence of items which are characterized by a specific attribute has to be processed by the service station which benefits from consecutive items with the same attribute value. We use the buffer to minimize the cost of service station i.e., the output sequence has maximal subsequences of items with the same attribute. This problem has many applications in computer science and economics. In this seminar, we will visit many variants of RBM problem, introduce the algorithms that produced and discuss the complexity analysis.
Danışman Ad Soyad: Muhammed Oğuzhan Külekçi
Sunum Tarihi: 26.11.2019
Sunum Saati: 10.15

Öğrenci No: 704181005
Ad Soyad: Behnaz Ghaderkalankesh
Başlık: Can text mining be an antidote for radiology error?
Özet/Abstract: Radiology reports contain detailed information about patients’ health status. This critical patient health data is recorded as free text format. Radiology reports differ based on the experiences of the radiologists and, the normative information given by the medical school. Especially, the accuracy rate in radiology reports of cancer patients is a critical issue. We want to develop an unsupervised machine learning model to understand the main concepts in a radiology report based on their similarity. In this model, we collect cancer diagnosed reports of head, neck and abdomen region. Based on the model, we try to understand which classification method is more effective in classifying low sample sized radiology reports. All in all, we aimed to implement and train a model that can be easily utilized for rare disease and cancers that normally a sufficient amount of biomedical information and database is not exist.
Danışman Ad Soyad: Sefer Baday
Sunum Tarihi: 26.11.2019
Sunum Saati: 11.00