2 citations · 2 across the 7 of their papers we have counts for
7 papers
Variance-Preserving Orthogonal Selection (VPOS): Greedy Feature Selection via Orthogonal Deflation in PCA Loading Space
Baran Koseoglu, Berrin Yanikoglu
We present Variance-Preserving Orthogonal Selection (VPOS), an unsupervised feature-selection method that performs sequential orthogonal deflation in the variance-weighted principa…
Hate Speech Detection in Turkish and Arabic: A Comprehensive Study
Somaiyeh Dehghan, Gökçe Uludoğan, Mehmet Umut Şen +3
Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing. Societies grappling…
Evaluating the Efficiency of Latent Spaces via the Coupling-Matrix
Mehmet Can Yavuz, Berrin Yanikoglu
A central challenge in representation learning is constructing latent embeddings that are both expressive and efficient. In practice, deep networks often produce redundant latent s…
Variational Self-Supervised Learning
Mehmet Can Yavuz, Berrin Yanikoglu
We present Variational Self-Supervised Learning (VSSL), a novel framework that combines variational inference with self-supervised learning to enable efficient, decoder-free repres…
Dealing with Annotator Disagreement in Hate Speech Classification
Somaiyeh Dehghan, Mehmet Umut Sen, Berrin Yanikoglu
Hate speech detection is a crucial task, especially on social media where harmful content can spread quickly. Collecting social media content (tweets etc.) to train machine learnin…
Going Forward-Forward in Distributed Deep Learning
Ege Aktemur, Ege Zorlutuna, Kaan Bilgili +3
We introduce a new approach in distributed deep learning, utilizing Geoffrey Hinton's Forward-Forward (FF) algorithm to speed up the training of neural networks in distributed comp…
Variational Self-Supervised Contrastive Learning Using Beta Divergence
Mehmet Can Yavuz, Berrin Yanikoglu
Learning a discriminative semantic space using unlabelled and noisy data remains unaddressed in a multi-label setting. We present a contrastive self-supervised learning method whic…