
Martin UrayFachhochschule Salzburg · MSc Program in Information Technology and Systems Management
Martin Uray
Master of Science
About
7
Publications
344
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14
Citations
Citations since 2017
Introduction
My current reserarch focuses on Natural Language Processing (NLP) and IR using Deep Learning and Reinforcment Learning.
Additional affiliations
May 2020 - present
October 2018 - March 2020
Education
September 2016 - October 2018
September 2013 - June 2016
Publications
Publications (7)
Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this upscaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about con...
Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about co...
In this paper we discuss the application of AI and ML to the exemplary industrial use case of the two-dimensional commissioning problem in a high-bay storage, which essentially can be phrased as an instance of Traveling Salesperson Problem (TSP). We investigate the mlrose library that provides an TSP optimizer based on various heuristic optimizatio...
The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep...
The application of neural networks has gotten more attention over the past few years. This trend affected research on contradiction detection, where the goal is to predict whether the information contained in a premise and hypothesis entail or contradict each other. The most novel and best performing approaches rely on neural networks. In this work...
The application of neural networks has gotten more attention over the past few years. This trend affected research on contradiction detection, where the goal is to predict whether the information contained in a premise and hypothesis entail or contradict each other. The most novel and best performing approaches rely on neural networks. In this work...