1 citations · 2 across the 6 of their papers we have counts for
6 papers
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting
Ozan Ozyegen, Juyoung Wang, Mucahit Cevik
Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in cert…
Interpretable Time Series Clustering Using Local Explanations
Ozan Ozyegen, Nicholas Prayogo, Mucahit Cevik +1
This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not direc…
Text Classification for Predicting Multi-level Product Categories
Hadi Jahanshahi, Ozan Ozyegen, Mucahit Cevik +4
In an online shopping platform, a detailed classification of the products facilitates user navigation. It also helps online retailers keep track of the price fluctuations in a cert…
Word-level Text Highlighting of Medical Texts for Telehealth Services
Ozan Ozyegen, Devika Kabe, Mucahit Cevik
The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedi…
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting
Ozan Ozyegen, Igor Ilic, Mucahit Cevik
Being able to interpret a machine learning model is a crucial task in many applications of machine learning. Specifically, local interpretability is important in determining why a…
An empirical study on using CNNs for fast radio signal prediction
Ozan Ozyegen, Sanaz Mohammadjafari, Karim El mokhtari +3
Accurate radio frequency power prediction in a geographic region is a computationally expensive part of finding the optimal transmitter location using a ray tracing software. We em…