activity
20162021
most citedGAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning

26 citations · 84 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SP2021★ 12 cited

A Time-Temperature Dataset for the Strawberry Cold Chain Across Multiple Shipments and Locations

Alla Abdella, Jeffrey K. Brecht, Ismail Uysal

This article describes location aware temperature profiles from six strawberry shipments across the continental United States. Three pallets were instrumented in each shipment with…

eess.SP2019★ 11 cited

Bio-inspired Filter Banks for Frequency Recognition of SSVEP-based Brain-computer Interfaces

Ali Fatih Demir, Hüseyin Arslan, Ismail Uysal

Brain-computer interfaces (BCIs) and their associated technologies have the potential to shape future forms of communication, control, and security. Specifically, the steady-state…

cs.LG2018★ 23 cited

Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization

Ozsel Kilinc, Ismail Uysal

In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specif…

cs.LG2017★ 26 cited

GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning

Ozsel Kilinc, Ismail Uysal

In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised port…

cs.LG2017

Clustering-based Source-aware Assessment of True Robustness for Learning Models

Ozsel Kilinc, Ismail Uysal

We introduce a novel validation framework to measure the true robustness of learning models for real-world applications by creating source-inclusive and source-exclusive partitions…

cs.LG2017★ 4 cited

Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks

Ozsel Kilinc, Ismail Uysal

In this paper, we discuss a different type of semi-supervised setting: a coarse level of labeling is available for all observations but the model has to learn a fine level of laten…

cs.HC2016★ 8 cited

Bio-Inspired Filter Banks for SSVEP-based Brain-Computer Interfaces

A. Fatih Demir, Huseyin Arslan, Ismail Uysal

Brain-computer interfaces (BCI) have the potential to play a vital role in future healthcare technologies by providing an alternative way of communication and control. More specifi…