129 citations · 390 across the 19 of their papers we have counts for
23 papers
When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection
Mehmet Yamaç, Yagmur Mustu, Muhammad Numan Yousaf +2
Reconstruction-based unsupervised learning can fail in two opposing ways: a model may reconstruct anomalies too accurately or discard valid nominal variation. Using the Pursuit of…
From Reports to Ontologies: Ontology-Guided Representation Learning for 12-Lead ECG
Lei Xu, Fahad Sohrab, Mehmet Yamac +2
The 12-lead electrocardiogram (ECG) is a quasi-periodic, multi-channel signal with diagnostic content spanning timescales from millisecond waveform morphology to multi-second rhyth…
Axiomatizing Neural Networks via Pursuit of Subspaces
Mehmet Yamac, Mert Duman, Ugur Akpinar +4
While deep neural networks have achieved remarkable success across a wide range of domains, their underlying mechanisms remain poorly understood, and they are often regarded as bla…
MTS-CSNet: Multiscale Tensor Factorization for Deep Compressive Sensing on RGB Images
Mehmet Yamac, Lei Xu, Serkan Kiranyaz +1
Deep learning based compressive sensing (CS) methods typically learn sampling operators using convolutional or block wise fully connected layers, which limit receptive fields and s…
Expert Operational GANS: Towards Real-Color Underwater Image Restoration
Ozer Can Devecioglu, Serkan Kiranyaz, Mehmet Yamac +1
The wide range of deformation artifacts that arise from complex light propagation, scattering, and depth-dependent attenuation makes the underwater image restoration to remain a ch…
Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection
Lei Xu, Mehmet Yamac, Mete Ahishali +1
Edge detection has attracted considerable attention thanks to its exceptional ability to enhance performance in downstream computer vision tasks. In recent years, various deep lear…
Multiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing
Mehmet Yamaç, Muhammad Numan Yousaf, Serkan Kiranyaz +1
Multilayer perceptrons (MLP), or fully connected artificial neural networks, are known for performing vector-matrix multiplications using learnable weight matrices; however, their…
Operational Support Estimator Networks
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz +1
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the l…
Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring
Dan Yang, Mehmet Yamac
Event cameras differ from conventional RGB cameras in that they produce asynchronous data sequences. While RGB cameras capture every frame at a fixed rate, event cameras only captu…
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
Alexander Ulrichsen, Paul Murray, Stephen Marshall +4
Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information com…
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
Mehmet Yamaç, Mert Duman, İlke Adalıoğlu +2
This paper proposes a low-cost and highly accurate ECG-monitoring system intended for personalized early arrhythmia detection for wearable mobile sensors. Earlier supervised approa…
Super Neurons
Serkan Kiranyaz, Junaid Malik, Mehmet Yamac +5
Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neuro…
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation
Mehmet Yamac, Ugur Akpinar, Erdem Sahin +2
In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruct…
Representation Based Regression for Object Distance Estimation
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz +1
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional…
BM3D vs 2-Layer ONN
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac +1
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient met…
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac +2
Real-world blind denoising poses a unique image restoration challenge due to the non-deterministic nature of the underlying noise distribution. Prevalent discriminative networks tr…
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
Aysen Degerli, Mete Ahishali, Mehmet Yamac +6
Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numer…
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Mete Ahishali, Aysen Degerli, Mehmet Yamac +6
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning…
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Mehmet Yamac, Mete Ahishali, Aysen Degerli +3
Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected seve…
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Mehmet Yamac, Mete Ahishali, Serkan Kiranyaz +1
Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing wi…
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Mehmet Yamac, Mete Ahishali, Nikolaos Passalis +3
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innov…
Multilinear Compressive Learning
Dat Thanh Tran, Mehmet Yamac, Aysen Degerli +2
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of mea…
Compressively Sensed Image Recognition
Aysen Degerli, Sinem Aslan, Mehmet Yamac +2
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it r…