Interpreting Variational Autoencoders with Fuzzy Logic: A step towards interpretable deep learning based fuzzy classifiers. - Kutay Bölat; Tufan Kumbasar (FUZZ-IEEE, 2020)

Channel Measurements and Modeling in Drone-to-Ground Communications. - Okan Ertürk; Kutay Bölat; Ayse Betül Büyüksar; A. Doruk Baskan; Ibrahim Altunbas (SIU, 2020)

Integrating Interval Type-2 Fuzzy Sets into Deep Embedding Clustering to Cope with Uncertainty. - Kutay Bölat; Tufan Kumbasar (FUZZ-IEEE, 2021)

Channel State Information Based Localization with Deep Learning. - Kutay Bölat (CoRR, 2021)

Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective. - Kutay Bölat; Simon H. Tindemans; Peter Palensky (CoRR, 2023)

GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries. - Kutay Bölat; Simon H. Tindemans (CoRR, 2024)

Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders. - Konrad Sundsgaard; Kutay Bölat; Guangya Yang (CoRR, 2025)

One Model to Forecast Them All and in Entity Distributions Bind Them. - Kutay Bölat; Simon H. Tindemans (CoRR, 2025)

Clustering Rooftop PV Systems via Probabilistic Embeddings. - Kutay Bölat; Tarek AlSkaif; Peter Palensky; Simon H. Tindemans (CoRR, 2025)