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1Stability selection enables robust learning of partial differential equations from limited noisy data
S. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. (2019)
4 months ago by @scadsfct
show all tags
(physics.data-an),
Analysis,
Probability
Data
sciences,
Numerical
Statistics
FOS:
Machine
Physical
sciences
Analysis
(cs.LG),
Learning
(math.NA),
Mathematics,
and
Computer
information
(physics.data-an),Analysis,ProbabilityDatasciences,NumericalStatisticsFOS:MachinePhysicalsciencesAnalysis(cs.LG),Learning(math.NA),Mathematics,andComputerinformation
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1Sampling Bias Due to Near-Duplicates in Learning to Rank
M. Fröbe, J. Bevendorff, J. Reimer, M. Potthast, and M. Hagen. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, page 1997–2000. New York, NY, USA, Association for Computing Machinery, (2020)
5 months ago by @scadsfct
show all tags
principle,
near-duplicate-detection,
selection
novelty
bias
learning
to
rank,
principle,near-duplicate-detection,selectionnoveltybiaslearningtorank,
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1Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, and H. Wachsmuth. 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP, page 184--189. Association for Computational Linguistics, (November 2021)
a month ago by @scadsfct
show all tags
Analysis
Learning
Point
Argument
Extractive
Zno
Contrastive
Key
Summarization
AnalysisLearningPointArgumentExtractiveZnoContrastiveKeySummarization
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1Common features in lncRNA annotation and classification: A survey
C. Klapproth, R. Sen, P. Stadler, S. Findeiß, and J. Fallmann. Noncoding RNA, 7 (4): 77 (December 2021)
5 months ago by @scadsfct
show all tags
problems;
coding
extraction;
feature
sequence;
machine
lncRNA;
learning
classification
problems;codingextraction;featuresequence;machinelncRNA;learningclassification
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1Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp"
J. Riedel, and J. Kleppsch. (2021)
5 months ago by @scadsfct
show all tags
Human
Bewertung,
Empirical
Mediennutzung,
Lernprozess,
Untersuchung,
Use
Activities),
und
Qualitative
Hochschullehre,
institute,
intelligence,
being,
Project,
student,
Vergleich,
Bildungswesen,
Projekt,
process,
Judgment,
Medien,
research
Erziehung,
Mensch,
Judgement,
Utilisation
Student,
Forschung,
Nachteil,
Hochschule,
Empirische
Technologie,
Vorteil,
Education,
Utilization
Assessment,
Intelligenz,
University
media,
Higher
Schul-
Artificial
Medieneinsatz,
(Learning
Deployment
Male
Projects
370
education
K\"{u}nstliche
Technology,
teaching,
lecturing,
Learning
Digitale
study,
of
HumanBewertung,EmpiricalMediennutzung,Lernprozess,Untersuchung,UseActivities),undQualitativeHochschullehre,institute,intelligence,being,Project,student,Vergleich,Bildungswesen,Projekt,process,Judgment,Medien,researchErziehung,Mensch,Judgement,UtilisationStudent,Forschung,Nachteil,Hochschule,EmpirischeTechnologie,Vorteil,Education,UtilizationAssessment,Intelligenz,Universitymedia,HigherSchul-ArtificialMedieneinsatz,(LearningDeploymentMaleProjects370educationK\"{u}nstlicheTechnology,teaching,lecturing,LearningDigitalestudy,of
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1Stability selection enables robust learning of differential equations from limited noisy data
S. Maddu, {. Cheeseman, {. Sbalzarini, and {. Müller. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (June 2022)Publisher Copyright: © 2022 Royal Society Publishing. All rights reserved..
3 months ago by @scadsfct
show all tags
FIS_scads
PAR
statistical
proteins,
equations,
learning
selection,
sparse
differential
regression,
learning,
machine
topic_lifescience
stability
theory
FIS_scadsPARstatisticalproteins,equations,learningselection,sparsedifferentialregression,learning,machinetopic_lifesciencestabilitytheory
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1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
N. Ghaffari Laleh, H. Muti, C. Loeffler, A. Echle, O. Saldanha, F. Mahmood, M. Lu, C. Trautwein, R. Langer, B. Dislich and 13 other author(s). Med. Image Anal., 79 (102474): 102474 (July 2022)
5 months ago by @scadsfct
show all tags
Convolutional
Artificial
intelligence;
Multiple-Instance
deep
neural
networks;
learning
Weakly-supervised
Learning;
transformers;
pathology;
topic_lifescience
Vision
Computational
ConvolutionalArtificialintelligence;Multiple-Instancedeepneuralnetworks;learningWeakly-supervisedLearning;transformers;pathology;topic_lifescienceVisionComputational
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2Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse
M. Bogdan. XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), Servizo de Publicacións da UDC, (September 2022)
a month ago by @scadsfct
show all tags
Learning
neural
algorithms
spiking
Xack
learning
plausible
networks
neurological
LearningneuralalgorithmsspikingXacklearningplausiblenetworksneurological
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2PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning
L. Monteiro, J. Saraiva, R. Toscan, P. Stadler, R. Silva-Rocha, and U. Nunes da Rocha. Environmental Microbiome, (December 2022)
5 months ago by @scadsfct
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microbialdeep
bacterial
PredicTF:
transcription
complex
prediction
learning
microbialdeepbacterialPredicTF:transcriptioncomplexpredictionlearning
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1Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain
S. Hofmann, F. Beyer, S. Lapuschkin, O. Goltermann, M. Loeffler, K. M√ºller, A. Villringer, W. Samek, and A. Witte. NeuroImage, (2022)
5 months ago by @scadsfct
show all tags
Cardiovascular
deep
Brain-age,
Structural
learning
Ageing,
a.i.,
mri,
Explainable
topic_lifescience
topic_neuroinspired
risk
factors,
CardiovasculardeepBrain-age,StructurallearningAgeing,a.i.,mri,Explainabletopic_lifesciencetopic_neuroinspiredriskfactors,
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1Continual learning autoencoder training for a particle-in-cell simulation via streaming
P. Stiller, V. Makdani, F. Pöschel, R. Pausch, A. Debus, M. Bussmann, and N. Hoffmann. (2022)
a month ago by @scadsfct
show all tags
imported
autoencoder
particle-in-cell
simulation
learning
training
Zno
Continual
importedautoencoderparticle-in-cellsimulationlearningtrainingZnoContinual
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1WARC-DL: Scalable Web Archive Processing for Deep Learning
N. Deckers, and M. Potthast. (2022)
a month ago by @scadsfct
show all tags
Archive
WARC-DL
Web
Learning
Scalable
Xack
Deep
Processing
ArchiveWARC-DLWebLearningScalableXackDeepProcessing
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1Named Entity Recognition for Specific Domains - Take Advantage of Transfer Learning
S. Torge, W. Hahn, L. Manjunath, and R. Jäkel. International Journal of Information Science and Technology, (2022)
a month ago by @scadsfct
show all tags
Entity
Domains
Learning
Advantage
Specific
Transfer
Xack
Recognition
EntityDomainsLearningAdvantageSpecificTransferXackRecognition
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1Steuerung von Compliant-Mechanismen durch Reinforcement Learning
L. Muschalski, J. Wollmann, A. Hornig, and N. Modler. GETRIEBETAGUNG 2022, (2022)
3 months ago by @scadsfct
show all tags
Learning
Compliant-Mechanismen
Steuerung
Reinforcement
topic_engineering
LearningCompliant-MechanismenSteuerungReinforcementtopic_engineering
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1Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection
D. Kiakou, A. Adamopoulos, and N. Scherf. Worldwide Congress on “Genetics, Geriatrics and Neurodegenerative Diseases Research", page 223--230. Springer, (2022)
3 months ago by @scadsfct
show all tags
Neuroimaging
Investigating
Selection
Impact
Disease
Graph-Based
Prediction
topic_neuroinspired
learning
Feature
NeuroimagingInvestigatingSelectionImpactDiseaseGraph-BasedPredictiontopic_neuroinspiredlearningFeature
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1Deep reinforcement learning with artificial microswimmers
R. Pradip, and F. Cichos. Emerging Topics in Artificial Intelligence (ETAI) 2022, 12204, page 104--110. SPIE, (2022)
3 months ago by @scadsfct
show all tags
microswimmers
artificial
learning
topic_physchemistry
reinforcement
Deep
microswimmersartificiallearningtopic_physchemistryreinforcementDeep
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1Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning
S. Adama, S. Wu, N. Nicolaou, and M. Bogdan. SNE Simul. Notes Eur., 32 (1): 37--45 (2022)
a month ago by @scadsfct
show all tags
hybrid
machine
patient
{CLIS}
learning
Zno
states
conscious
hybridmachinepatient{CLIS}learningZnostatesconscious
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1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach
M. Steinacker, Y. Kheifetz, and M. Scholz. Heliyon, 9 (7): e17890 (July 2023)
5 months ago by @scadsfct
show all tags
neural
networks;
identification;
Recurrent
learning
Haematopoiesis;
unit_transfer
System
Precision
topic_mathfoundation
topic_neuroinspired
Transfer
medicine;
neuralnetworks;identification;RecurrentlearningHaematopoiesis;unit_transferSystemPrecisiontopic_mathfoundationtopic_neuroinspiredTransfermedicine;
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1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures
N. Charaf, J. Haase, A. Kulisch, {. Elm, and D. Göhringer. 2023 26th Euromicro Conference on Digital System Design (DSD), page 585--592. IEEE, (Sep 8, 2023)Publisher Copyright: © 2023 IEEE.; 26th Euromicro Conference on Digital System Design, DSD 2023 ; Conference date: 06-09-2023 Through 08-09-2023.
3 months ago by @scadsfct
show all tags
FIS_scads
Shape
learning
Machine
computing,
topic_federatedlearn
Runtime,
vision,
algorithms,
architecture,
Embedded
Computer
Scalability,
FIS_scadsShapelearningMachinecomputing,topic_federatedlearnRuntime,vision,algorithms,architecture,EmbeddedComputerScalability,
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2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.
M. Bauer, and C. Augenstein. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, volume 35 of FedCSIS 2023, page 93–101. IEEE, (September 2023)
2 months ago by @scadsfct
show all tags
Study
Learning
Computer
Data
Self-Supervised
Unlabelled
Vision.
Comparative
StudyLearningComputerDataSelf-SupervisedUnlabelledVision.Comparative
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publications (hide)41
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1Stability selection enables robust learning of partial differential equations from limited noisy data

1Sampling Bias Due to Near-Duplicates in Learning to Rank

1Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

1Common features in lncRNA annotation and classification: A survey

1Wie bereit sind Studierende für die Nutzung von KI-Technologien? Eine Annäherung an die KI-Readiness Studierender im Kontext des Projektes "tech4comp"

1Stability selection enables robust learning of differential equations from limited noisy data

1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

2Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse

2PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning

1Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

1Continual learning autoencoder training for a particle-in-cell simulation via streaming

1WARC-DL: Scalable Web Archive Processing for Deep Learning

1Named Entity Recognition for Specific Domains - Take Advantage of Transfer Learning

1Steuerung von Compliant-Mechanismen durch Reinforcement Learning

1Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection

1Deep reinforcement learning with artificial microswimmers

1Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning

1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures

2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

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