no code implementations • 30 Nov 2022 • Arda Fazla, Mustafa Enes Aydin, Orhun Tamyigit, Suleyman Serdar Kozat
Unlike all the literature in ensembling, for the first time, we introduce a new approach using a meta learner that effectively combines the base model predictions via using a superset of the features that is the union of the base models' feature vectors instead of the predictions themselves.
1 code implementation • 29 Nov 2021 • Selim Furkan Tekin, Suleyman Serdar Kozat
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions.
1 code implementation • 24 Sep 2021 • Baturay Saglam, Furkan Burak Mutlu, Dogan Can Cicek, Suleyman Serdar Kozat
We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic approaches that overcome the overestimation bias lead to a substantial underestimation bias.
2 code implementations • 1 Feb 2021 • Selim Furkan Tekin, Arda Fazla, Suleyman Serdar Kozat
To this end, we introduce a novel deep learning architecture for forecasting high-resolution spatio-temporal weather data.
no code implementations • 17 Jun 2020 • Fatih Ilhan, Oguzhan Karaahmetoglu, Ismail Balaban, Suleyman Serdar Kozat
We investigate nonlinear regression for nonstationary sequential data.
no code implementations • 25 May 2020 • Oguzhan Karaahmetoglu, Fatih Ilhan, Ismail Balaban, Suleyman Serdar Kozat
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values.
no code implementations • 7 Mar 2020 • Fatih Ilhan, Suleyman Serdar Kozat
We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process.
no code implementations • 7 Mar 2020 • Oguzhan Karaahmetoglu, Suleyman Serdar Kozat
We extend the formulations of a standard point process model that can represent time-series data to represent a spatio-temporal data.
no code implementations • 29 May 2019 • Hakan Gokcesu, Kaan Gokcesu, Suleyman Serdar Kozat
We study the min-max optimization problem where each function contributing to the max operation is strongly-convex and smooth with bounded gradient in the search domain.
no code implementations • 9 Mar 2018 • Mohammadreza Mohaghegh Neyshabouri, Suleyman Serdar Kozat
In order to construct our multi-modal density function, we use an incremental decision tree to construct a set of subspaces of the observation space.
no code implementations • 25 Oct 2017 • Tolga Ergen, Ali Hassan Mirza, Suleyman Serdar Kozat
We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms.
Semi-supervised Anomaly Detection Supervised Anomaly Detection +1
no code implementations • 6 Jan 2016 • Dariush Kari, Nuri Denizcan Vanli, Suleyman Serdar Kozat
We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance.
no code implementations • 19 Dec 2015 • Dariush Kari, Muhammed Omer Sayin, Suleyman Serdar Kozat
We introduce a novel family of adaptive robust equalizers for highly challenging underwater acoustic (UWA) channel equalization.