Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Santos Tiago, Schrunner Stefan, Geiger Bernhard, Pfeiler Olivia, Zernig Anja, Kaestner Andre, Kern Roman

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Mode

IEEE Transactions on Semiconductor Manufacturing, IEEE, 2019

Journal
Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results
2019

Fuchs Alexandra, Geiger Bernhard, Hobisch Elisabeth, Koncar Philipp, Saric Sanja, Scholger Martina

Distant Spectators: Mining TEI-encoded periodicals of the Enlightenment

TEI Conf. and Member's Meeting, Graz, 2019

Konferenz
with contributions from Denis Helic and Jacqueline More
2019

Lindstaedt Stefanie , Geiger Bernhard, Pirker Gerhard

Big Data and Data Driven Modeling - A New Dawn for Engine Operation and Development

17th Symp. The Working Process of the Internal Combustion Engine, Graz, 2019

Konferenz
2019

Geiger Bernhard, Koch Tobias

On the Information Dimension of Stochastic Processes

IEEE Transactions on Information Theory, IEEE, 2019

Journal
2019

Schweimer Christoph, Geiger Bernhard, Suleimenova Diana, Groen Derek, Gfrerer Christine, Pape David, Elsaesser Robert, Kocsis Albert Tihamér, Liszkai B., Horváth Zoltan

Model Reduction in HiDALGO - Initial Plans and Ideas

Workshop on Model Reduction of Complex Dynamical Systems (MODRED), Graz, 2019

Konferenz
2019

Toller Maximilian, Geiger Bernhard, Kern Roman

A Formally Robust Time Series Distance Metric

Mile'TS @ SIGKDD, Anchorage, Alaska USA, 2019

Konferenz
Distance-based classification is among the most competitive classification methods for time series data. The most critical componentof distance-based classification is the selected distance function.Past research has proposed various different distance metrics ormeasures dedicated to particular aspects of real-world time seriesdata, yet there is an important aspect that has not been considered so far: Robustness against arbitrary data contamination. In thiswork, we propose a novel distance metric that is robust against arbitrarily “bad” contamination and has a worst-case computationalcomplexity of O(n logn). We formally argue why our proposedmetric is robust, and demonstrate in an empirical evaluation thatthe metric yields competitive classification accuracy when appliedin k-Nearest Neighbor time series classification.
2019

Geiger Bernhard

On the Information Dimension of Random Variables and Stochastic Processes

Workshop on Casualty and Dynamics in Brain Networks @ Int. Joint Conf. on Neural Networks, Budapest, 2019

Konferenz
joint work with Tobias Koch, Universidad Carlos III de Madrid
2019

Tiago Santos, Stefan Schrunner, Geiger Bernhard, Olivia Pfeiler, Anja Zernig, Andre Kaestner, Kern Roman

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Mode

IEEE Transactions on Semiconductor Manufacturing, IEEE, 2019

Journal
Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results
2019

Maritsch Martin, Diana Suleimenova, Geiger Bernhard, Derek Groen

AI-Support for large-scale Refugee Movement Simulations

Computing Systems Week Spring 2019, HiPEAC, Edinburgh, 2019

Konferenz
2019

Geiger Bernhard, Schrunner Stefan, Kern Roman

An Information-Theoretic Measure for Pattern Similarity in Analog Wafermap

European Advanced Process Control and Manufacturing Conf. (apc|m, Villach, 2019

Konferenz
Schrunner and Geiger have contributed equally to this work.
2019

Clemens Bloechl, Rana Ali Amjad, Geiger Bernhard

Co-Clustering via Information-Theoretic Markov Aggregation

IEEE Transactions on Knowledge and Data Engineering, IEEE, 2019

Journal
We present an information-theoretic cost function for co-clustering, i.e., for simultaneous clustering of two sets based on similarities between their elements. By constructing a simple random walk on the corresponding bipartite graph, our cost function is derived from a recently proposed generalized framework for information-theoretic Markov chain aggregation. The goal of our cost function is to minimize relevant information loss, hence it connects to the information bottleneck formalism. Moreover, via the connection to Markov aggregation, our cost function is not ad hoc, but inherits its justification from the operational qualities associated with the corresponding Markov aggregation problem. We furthermore show that, for appropriate parameter settings, our cost function is identical to well-known approaches from the literature, such as “Information-Theoretic Co-Clustering” by Dhillon et al. Hence, understanding the influence of this parameter admits a deeper understanding of the relationship between previously proposed information-theoretic cost functions. We highlight some strengths and weaknesses of the cost function for different parameters. We also illustrate the performance of our cost function, optimized with a simple sequential heuristic, on several synthetic and real-world data sets, including the Newsgroup20 and the MovieLens100k data sets
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