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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 @scads.ai
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Learning
neural
algorithms
spiking
learning
from:scadsfct
plausible
networks
neurological
Learningneuralalgorithmsspikinglearningfrom:scadsfctplausiblenetworksneurological
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2Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient
O. Kirsten, M. Bogdan, and S. Adama. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), page 37-41. (2023)
a month ago by @scads.ai
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models
Consciousness
Prediction
from:scadsfct
Syndrome
Predictive
processing
modeling
Theory
algorithms
data
learning
Information
Complexity
Modeling
Processing
Machine
Learning
Neuroscience
Training
Measures
Locked-In
and
Computational
Signal
modelsConsciousnessPredictionfrom:scadsfctSyndromePredictiveprocessingmodelingTheoryalgorithmsdatalearningInformationComplexityModelingProcessingMachineLearningNeuroscienceTrainingMeasuresLocked-InandComputationalSignal
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2BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification
A. Avila Santos, B. de Almeida, R. Bonidia, P. Stadler, P. Stefanic, I. Mandic-Mulec, U. Rocha, D. Sanches, and A. de Carvalho. RNA Biology, 21 (1): 410–421 (March 2024)
2 months ago by @scads.ai
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RNA
deep
non-coding
feature
learning
from:scadsfct
extraction
BioDeepfuse
classification
RNAdeepnon-codingfeaturelearningfrom:scadsfctextractionBioDeepfuseclassification
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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)
2 months ago by @scads.ai
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microbialdeep
bacterial
PredicTF:
transcription
complex
learning
prediction
from:scadsfct
microbialdeepbacterialPredicTF:transcriptioncomplexlearningpredictionfrom:scadsfct
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2Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning
J. Richards, R. Huser, E. Bevacqua, and J. Zscheischler. Artificial Intelligence for the Earth Systems, (October 2023)
2 months ago by @scads.ai
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Extreme
Mediterranean
Learning
Wildfires
Spatiotemporal
Trends
Statistical
Deep
from:scadsfct
ExtremeMediterraneanLearningWildfiresSpatiotemporalTrendsStatisticalDeepfrom:scadsfct
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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 @scads.ai
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Study
Learning
Computer
Data
Self-Supervised
from:scadsfct
Unlabelled
Comparative
Vision.
StudyLearningComputerDataSelf-Supervisedfrom:scadsfctUnlabelledComparativeVision.
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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

2Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient

2BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

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

2Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning

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

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