---
_id: '52816'
abstract:
- lang: eng
text: "Manufacturing companies face the challenge of reaching required quality standards.
Using\r\noptical sensors and deep learning might help. However, training deep
learning algorithms\r\nrequire large amounts of visual training data. Using domain
randomization to generate synthetic\r\nimage data can alleviate this bottleneck.
This paper presents the application of synthetic\r\nimage training data for optical
quality inspections using visual sensor technology. The results\r\nshow synthetically
generated training data are appropriate for visual quality inspections."
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Michael
full_name: Hieb, Michael
id: '72252'
last_name: Hieb
citation:
ama: 'Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in
Machine Vision Quality Gates in Manufacturing. In: Lectures. AMA Service
GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:10.5162/smsi2023/d7.4'
apa: Gräßler, I., & Hieb, M. (2023). Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing. Lectures,
253–524. https://doi.org/10.5162/smsi2023/d7.4
bibtex: '@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={10.5162/smsi2023/d7.4},
booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515
Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524}
}'
chicago: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing.” In Lectures,
253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023.
https://doi.org/10.5162/smsi2023/d7.4.
ieee: 'I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection
in Machine Vision Quality Gates in Manufacturing,” in Lectures, Nuremberg,
2023, pp. 253–524, doi: 10.5162/smsi2023/d7.4.'
mla: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing.” Lectures,
AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp.
253–524, doi:10.5162/smsi2023/d7.4.
short: 'I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str.
49, 31515 Wunstorf, Germany, 2023, pp. 253–524.'
conference:
end_date: 2023-05-11
location: Nuremberg
name: SMSI 2023. Sensor and Measurement Science International
start_date: 2023-05-08
date_created: 2024-03-25T10:16:24Z
date_updated: 2024-03-25T11:05:53Z
department:
- _id: '152'
doi: 10.5162/smsi2023/d7.4
keyword:
- synthetic training data
- machine vision quality gates
- deep learning
- automated inspection and quality control
- production control
language:
- iso: eng
page: 253-524
publication: Lectures
publication_status: published
publisher: AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany
quality_controlled: '1'
status: public
title: Creating Synthetic Training Datasets for Inspection in Machine Vision Quality
Gates in Manufacturing
type: conference
user_id: '5905'
year: '2023'
...
---
_id: '20212'
abstract:
- lang: eng
text: "Ideational impact refers to the uptake of a paper's ideas and concepts by
subsequent research. It is defined in stark contrast to total citation impact,
a measure predominantly used in research evaluation that assumes that all citations
are equal. Understanding ideational impact is critical for evaluating research
impact and understanding how scientific disciplines build a cumulative tradition.
Research has only recently developed automated citation classification techniques
to distinguish between different types of citations and generally does not emphasize
the conceptual content of the citations and its ideational impact. To address
this problem, we develop Deep Content-enriched Ideational Impact Classification
(Deep-CENIC) as the first automated approach for ideational impact classification
to support researchers' literature search practices. We evaluate Deep-CENIC on
1,256 papers citing 24 information systems review articles from the IT business
value domain. We show that Deep-CENIC significantly outperforms state-of-the-art
benchmark models. We contribute to information systems research by operationalizing
the concept of ideational impact, designing a recommender system for academic
papers based on deep learning techniques, and empirically exploring the ideational
impact of the IT business value domain.\r\n"
article_number: '113432'
author:
- first_name: Julian
full_name: Prester, Julian
last_name: Prester
- first_name: Gerit
full_name: Wagner, Gerit
last_name: Wagner
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Nik Rushdi
full_name: Hassan, Nik Rushdi
last_name: Hassan
citation:
ama: 'Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact
of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.
Decision Support Systems. 2021;140(January).'
apa: 'Prester, J., Wagner, G., Schryen, G., & Hassan, N. R. (2021). Classifying
the Ideational Impact of Information Systems Review Articles: A Content-Enriched
Deep Learning Approach. Decision Support Systems, 140(January),
Article 113432.'
bibtex: '@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational
Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
Approach}, volume={140}, number={January113432}, journal={Decision Support Systems},
author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi},
year={2021} }'
chicago: 'Prester, Julian, Gerit Wagner, Guido Schryen, and Nik Rushdi Hassan. “Classifying
the Ideational Impact of Information Systems Review Articles: A Content-Enriched
Deep Learning Approach.” Decision Support Systems 140, no. January (2021).'
ieee: 'J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational
Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
Approach,” Decision Support Systems, vol. 140, no. January, Art. no. 113432,
2021.'
mla: 'Prester, Julian, et al. “Classifying the Ideational Impact of Information
Systems Review Articles: A Content-Enriched Deep Learning Approach.” Decision
Support Systems, vol. 140, no. January, 113432, 2021.'
short: J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems
140 (2021).
date_created: 2020-10-27T13:28:21Z
date_updated: 2022-06-10T06:55:32Z
ddc:
- '000'
department:
- _id: '277'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2020-10-27T13:31:01Z
date_updated: 2020-10-27T13:31:01Z
file_id: '20213'
file_name: DECSUP-D-20-00312 - PREPUBLICATION.pdf
file_size: 440903
relation: main_file
file_date_updated: 2020-10-27T13:31:01Z
has_accepted_license: '1'
intvolume: ' 140'
issue: January
keyword:
- Ideational impact
- citation classification
- academic recommender systems
- natural language processing
- deep learning
- cumulative tradition
language:
- iso: eng
oa: '1'
publication: Decision Support Systems
status: public
title: 'Classifying the Ideational Impact of Information Systems Review Articles:
A Content-Enriched Deep Learning Approach'
type: journal_article
user_id: '72850'
volume: 140
year: '2021'
...
---
_id: '24547'
abstract:
- lang: eng
text: 'Over the last years, several approaches for the data-driven estimation of
expected possession value (EPV) in basketball and association football (soccer)
have been proposed. In this paper, we develop and evaluate PIVOT: the first such
framework for team handball. Accounting for the fast-paced, dynamic nature and
relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep
learning architecture that relies solely on tracking data. This efficient approach
is capable of predicting the probability that a team will score within the near
future given the fine-grained spatio-temporal distribution of all players and
the ball over the last seconds of the game. Our experiments indicate that PIVOT
is able to produce accurate and calibrated probability estimates, even when trained
on a relatively small dataset. We also showcase two interactive applications of
PIVOT for valuing actual and counterfactual player decisions and actions in real-time.'
author:
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Michael
full_name: Döring, Michael
last_name: Döring
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jochen
full_name: Baumeister, Jochen
id: '46'
last_name: Baumeister
orcid: 0000-0003-2683-5826
citation:
ama: 'Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious
End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking
Data. In: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021).'
apa: 'Müller, O., Caron, M., Döring, M., Heuwinkel, T., & Baumeister, J. (n.d.).
PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data. 8th Workshop on Machine Learning and Data
Mining for Sports Analytics (ECML PKDD 2021). European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021),
Online.'
bibtex: '@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining
for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew
and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }'
chicago: 'Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen
Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player
Actions in Handball Using Tracking Data.” In 8th Workshop on Machine Learning
and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
ieee: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data,” presented at the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.'
mla: 'Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework
for Valuing Player Actions in Handball Using Tracking Data.” 8th Workshop on
Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).'
short: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop
on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
conference:
end_date: 2021-09-17
location: Online
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery (ECML PKDD 2021)
start_date: 2021-09-13
date_created: 2021-09-16T08:33:04Z
date_updated: 2023-02-28T08:58:24Z
department:
- _id: '196'
- _id: '172'
keyword:
- expected possession value
- handball
- tracking data
- time series classification
- deep learning
language:
- iso: eng
main_file_link:
- url: https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf
publication: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021)
publication_status: inpress
status: public
title: 'PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data'
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '19609'
abstract:
- lang: eng
text: "Modern services comprise interconnected components,\r\ne.g., microservices
in a service mesh, that can scale and\r\nrun on multiple nodes across the network
on demand. To process\r\nincoming traffic, service components have to be instantiated
and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands
into account. This challenge is usually solved with\r\ncustom approaches designed
by experts. While this typically\r\nworks well for the considered scenario, the
models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin
practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement
learning approach that\r\nlearns how to best coordinate services and is geared
towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable,
possibly delayed monitoring information. Rather than\r\ndefining a complex model
or an algorithm how to achieve an\r\nobjective, our model-free approach adapts
to various objectives\r\nand traffic patterns. An agent is trained offline without
expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto
a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and
overall network utility on real-world network\r\ntopologies and traffic traces.
It also learns to optimize different\r\nobjectives, generalizes to scenarios with
unseen, stochastic traffic\r\npatterns, and scales to large real-world networks."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Adnan
full_name: Manzoor, Adnan
last_name: Manzoor
- first_name: Haydar
full_name: Qarawlus, Haydar
last_name: Qarawlus
- first_name: Rafael
full_name: Schellenberg, Rafael
last_name: Schellenberg
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Ramin
full_name: Khalili, Ramin
last_name: Khalili
- first_name: Artur
full_name: Hecker, Artur
last_name: Hecker
citation:
ama: 'Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service
Coordination Using Deep Reinforcement Learning. In: IEEE International Conference
on Network and Service Management (CNSM). IEEE; 2020.'
apa: Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili,
R., & Hecker, A. (2020). Self-Driving Network and Service Coordination Using
Deep Reinforcement Learning. In IEEE International Conference on Network and
Service Management (CNSM). IEEE.
bibtex: '@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020,
title={Self-Driving Network and Service Coordination Using Deep Reinforcement
Learning}, booktitle={IEEE International Conference on Network and Service Management
(CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan
and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin
and Hecker, Artur}, year={2020} }'
chicago: Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg,
Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service
Coordination Using Deep Reinforcement Learning.” In IEEE International Conference
on Network and Service Management (CNSM). IEEE, 2020.
ieee: S. B. Schneider et al., “Self-Driving Network and Service Coordination
Using Deep Reinforcement Learning,” in IEEE International Conference on Network
and Service Management (CNSM), 2020.
mla: Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination
Using Deep Reinforcement Learning.” IEEE International Conference on Network
and Service Management (CNSM), IEEE, 2020.
short: 'S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili,
A. Hecker, in: IEEE International Conference on Network and Service Management
(CNSM), IEEE, 2020.'
date_created: 2020-09-22T06:28:22Z
date_updated: 2022-01-06T06:54:08Z
ddc:
- '006'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2020-09-22T06:29:16Z
date_updated: 2020-09-22T06:36:00Z
file_id: '19610'
file_name: ris_with_copyright.pdf
file_size: 642999
relation: main_file
file_date_updated: 2020-09-22T06:36:00Z
has_accepted_license: '1'
keyword:
- self-driving networks
- self-learning
- network coordination
- service coordination
- reinforcement learning
- deep learning
- nfv
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE International Conference on Network and Service Management (CNSM)
publisher: IEEE
status: public
title: Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
type: conference
user_id: '35343'
year: '2020'
...
---
_id: '15580'
abstract:
- lang: eng
text: This paper deals with aspect phrase extraction and classification in sentiment
analysis. We summarize current approaches and datasets from the domain of aspect-based
sentiment analysis. This domain detects sentiments expressed for individual aspects
in unstructured text data. So far, mainly commercial user reviews for products
or services such as restaurants were investigated. We here present our dataset
consisting of German physician reviews, a sensitive and linguistically complex
field. Furthermore, we describe the annotation process of a dataset for supervised
learning with neural networks. Moreover, we introduce our model for extracting
and classifying aspect phrases in one step, which obtains an F1-score of 80%.
By applying it to a more complex domain, our approach and results outperform previous
approaches.
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Kersting J, Geierhos M. Aspect Phrase Extraction in Sentiment Analysis with
Deep Learning. In: Proceedings of the 12th International Conference on Agents
and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language
Processing in Artificial Intelligence (NLPinAI 2020). Setúbal, Portugal: SCITEPRESS;
2020:391--400.'
apa: 'Kersting, J., & Geierhos, M. (2020). Aspect Phrase Extraction in Sentiment
Analysis with Deep Learning. In Proceedings of the 12th International Conference
on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020) (pp. 391--400).
Setúbal, Portugal: SCITEPRESS.'
bibtex: '@inproceedings{Kersting_Geierhos_2020, place={Setúbal, Portugal}, title={Aspect
Phrase Extraction in Sentiment Analysis with Deep Learning}, booktitle={Proceedings
of the 12th International Conference on Agents and Artificial Intelligence (ICAART
2020) -- Special Session on Natural Language Processing in Artificial Intelligence
(NLPinAI 2020)}, publisher={SCITEPRESS}, author={Kersting, Joschka and Geierhos,
Michaela}, year={2020}, pages={391--400} }'
chicago: 'Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in
Sentiment Analysis with Deep Learning.” In Proceedings of the 12th International
Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session
on Natural Language Processing in Artificial Intelligence (NLPinAI 2020),
391--400. Setúbal, Portugal: SCITEPRESS, 2020.'
ieee: J. Kersting and M. Geierhos, “Aspect Phrase Extraction in Sentiment Analysis
with Deep Learning,” in Proceedings of the 12th International Conference on
Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020), Valetta, Malta,
2020, pp. 391--400.
mla: Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in Sentiment
Analysis with Deep Learning.” Proceedings of the 12th International Conference
on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS,
2020, pp. 391--400.
short: 'J. Kersting, M. Geierhos, in: Proceedings of the 12th International Conference
on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural
Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS, Setúbal,
Portugal, 2020, pp. 391--400.'
conference:
location: Valetta, Malta
name: International Conference on Agents and Artificial Intelligence (ICAART) -- Special
Session on Natural Language Processing in Artificial Intelligence (NLPinAI)
date_created: 2020-01-15T08:35:07Z
date_updated: 2022-01-06T06:52:29Z
ddc:
- '000'
department:
- _id: '579'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2020-09-18T09:27:00Z
date_updated: 2020-09-18T09:27:00Z
file_id: '19576'
file_name: Kersting & Geierhos (2020), Kersting2020.pdf
file_size: 421780
relation: main_file
success: 1
file_date_updated: 2020-09-18T09:27:00Z
has_accepted_license: '1'
keyword:
- Deep Learning
- Natural Language Processing
- Aspect-based Sentiment Analysis
language:
- iso: eng
page: 391--400
place: Setúbal, Portugal
project:
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
- _id: '9'
name: SFB 901 - Subproject B1
publication: Proceedings of the 12th International Conference on Agents and Artificial
Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in
Artificial Intelligence (NLPinAI 2020)
publisher: SCITEPRESS
status: public
title: Aspect Phrase Extraction in Sentiment Analysis with Deep Learning
type: conference
user_id: '58701'
year: '2020'
...
---
_id: '48897'
abstract:
- lang: eng
text: 'In this work we focus on the well-known Euclidean Traveling Salesperson Problem
(TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in
the context of per-instance algorithm selection (AS). We evolve instances with
nodes where the solvers show strongly different performance profiles. These instances
serve as a basis for an exploratory study on the identification of well-discriminating
problem characteristics (features). Our results in a nutshell: we show that even
though (1) promising features exist, (2) these are in line with previous results
from the literature, and (3) models trained with these features are more accurate
than models adopting sophisticated feature selection methods, the advantage is
not close to the virtual best solver in terms of penalized average runtime and
so is the performance gain over the single best solver. However, we show that
a feature-free deep neural network based approach solely based on visual representation
of the instances already matches classical AS model results and thus shows huge
potential for future studies.'
author:
- first_name: Moritz
full_name: Seiler, Moritz
last_name: Seiler
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive
Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson
Problem. In: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag;
2020:48–64. doi:10.1007/978-3-030-58112-1_4'
apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020).
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection
on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature}
(PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
bibtex: '@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin,
Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated
Algorithm Selection on the Traveling Salesperson Problem}, DOI={10.1007/978-3-030-58112-1_4},
booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag},
author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal
and Trautmann, Heike}, year={2020}, pages={48–64} }'
chicago: 'Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike
Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated
Algorithm Selection on the Traveling Salesperson Problem.” In Parallel Problem
Solving from {Nature} (PPSN XVI), 48–64. Berlin, Heidelberg: Springer-Verlag,
2020. https://doi.org/10.1007/978-3-030-58112-1_4.'
ieee: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning
as a Competitive Feature-Free Approach for Automated Algorithm Selection on the
Traveling Salesperson Problem,” in Parallel Problem Solving from {Nature} (PPSN
XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.'
mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach
for Automated Algorithm Selection on the Traveling Salesperson Problem.” Parallel
Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, 2020, pp. 48–64,
doi:10.1007/978-3-030-58112-1_4.
short: 'M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem
Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
48–64.'
date_created: 2023-11-14T15:59:00Z
date_updated: 2023-12-13T10:49:45Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_4
extern: '1'
keyword:
- Automated algorithm selection
- Deep learning
- Feature-based approaches
- Traveling Salesperson Problem
language:
- iso: eng
page: 48–64
place: Berlin, Heidelberg
publication: Parallel Problem Solving from {Nature} (PPSN XVI)
publication_identifier:
isbn:
- 978-3-030-58111-4
publisher: Springer-Verlag
status: public
title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm
Selection on the Traveling Salesperson Problem
type: conference
user_id: '102979'
year: '2020'
...