Search Results - "ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning"
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1
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Contributors: et al.
Source: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2025 ; 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ; https://hal.science/hal-05337708 ; 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society; Computer Vision Foundation (CVF), Jun 2025, Nashville, United States. pp.6216-6226, ⟨10.1109/CVPRW67362.2025.00619⟩ ; https://cvpr.thecvf.com/Conferences/2025
Subject Terms: Optical flow, Artificial intelligence, Motion Control, Generative IA, IA générative, Flux optique, Intelligence artificelle, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.10: Vision and Scene Understanding/I.2.10.4: Motion, ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.8: Scene Analysis/I.4.8.2: Motion, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Subject Geographic: Nashville, United States
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2411.10501; ARXIV: 2411.10501
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2
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Contributors: et al.
Source: ISSN: 2624-960X ; Quantum Reports ; https://hal.science/hal-05336361 ; Quantum Reports, 2025, 7 (4), pp.51. ⟨10.3390/quantum7040051⟩.
Subject Terms: machine learning (ML), electroencephalogram (EEG), brain-computer interface (BCI), optimization, nearest convex hull (NCH), quantum, Informatique quantique, Optimisation géométrique, Electroencephalographie EEG, Interface cerveau machine ICM, Apprentissage automatisé, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: K.: Computing Milieux, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO]Computer Science [cs], [PHYS.QPHY]Physics [physics]/Quantum Physics [quant-ph], [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences
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3
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Source: ISSN: 2352-1465 ; Transportation Research Procedia ; https://hal.science/hal-04210285 ; Transportation Research Procedia, 2025, 82, pp.3454-3471. ⟨10.1016/j.trpro.2024.12.083⟩ ; https://www.sciencedirect.com/science/article/pii/S2352146524003818.
Subject Terms: Transport Mode Detection, Smartphone sensors, GPS, Accelerometer, Machine Learning, Random Forest, Support Vector Machine, K-nearest neighbor, Multi layer perceptron, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
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4
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Contributors: et al.
Source: Neural Networks. 160:12-21
Subject Terms: ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning/I.2.6.6: Parameter learning, Convolutional neural network, 02 engineering and technology, [MATH.MATH-FA]Mathematics [math]/Functional Analysis [math.FA], 01 natural sciences, Deep learning Ensemble learning Sigmoid shrinkage Rectified linear unit Rectified sigmoid Parametric activation Convolutional neural network, Machine Learning, Sigmoid shrinkage, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Ensemble learning, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Rectified linear unit, Learnable activation, Rectified sigmoid, Deep learning, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Rectified sigmoid shrinkage unit, Parametric activation, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Neural Networks, Computer, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Access URL: https://pubmed.ncbi.nlm.nih.gov/36592526
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5
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Source: NeurIPS 2023 - Conference on Neural Information Processing Systems - Track on Datasets and Benchmarks ; https://hal.science/hal-04217697 ; NeurIPS 2023 - Conference on Neural Information Processing Systems - Track on Datasets and Benchmarks, Dec 2023, New Orleans (LA), United States
Subject Terms: Data, Video Understanding, Video Chapter Generation, Video Chapter Grounding, Dense Video Captioning, Pretraining, Language Model, Zero-Shot Learning, Computer Vision, Vision and Language, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.10: Vision and Scene Understanding, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Subject Geographic: New Orleans (LA), United States
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2309.13952; ARXIV: 2309.13952
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6
Authors: et al.
Contributors: et al.
Source: CVPR 2023 - IEEE/CVF Conference on Computer Vision and Pattern Recognition ; https://inria.hal.science/hal-04039246 ; CVPR 2023 - IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2023, Vancouver, Canada
Subject Terms: Computer Vision, Video Understanding, Dense Video Captioning, Video Captioning, Video Paragraph Captioning, Pretraining, Language Model, Few-Shot Learning, Vision and Language, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.10: Vision and Scene Understanding, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Subject Geographic: Vancouver
Time: Vancouver, Canada
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2302.14115; ARXIV: 2302.14115
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7
Authors: et al.
Contributors: et al.
Source: CAp2023 ; https://hal.science/hal-04123236 ; CAp2023, Jul 2023, Strasbourg (FRANCE), France
Subject Terms: deep metric learning, triplet loss, fair classifier, responsible AI, fairness, collapse, équité, apprentissage métrique, fonction de coût par triplet, classifieur équitable, IA responsable, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models, ACM: K.: Computing Milieux/K.4: COMPUTERS AND SOCIETY/K.4.2: Social Issues, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]
Subject Geographic: France
Time: Strasbourg (FRANCE), France
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2306.04400; hal-04123236; https://hal.science/hal-04123236; https://hal.science/hal-04123236/document; https://hal.science/hal-04123236/file/cap_camera_ready.pdf; ARXIV: 2306.04400
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8
Authors: et al.
Contributors: et al.
Source: https://hal.science/hal-04919622 ; 2024.
Subject Terms: Distributionally robust optim, distribution shifts, entropic regularization, 90C17, 90C15, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.4: Quadrature and Numerical Differentiation/G.1.4.1: Automatic differentiation, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.6: Optimization, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.5: Programming Languages and Software, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2410.21231; ARXIV: 2410.21231
Availability: https://hal.science/hal-04919622
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9
Authors: et al.
Contributors: et al.
Source: https://hal.science/hal-04791526 ; 2024.
Subject Terms: Transformer, Electroencephalograhy EEG, P300 classification, Deep learning, Event-related potential ERP, Signal analysis, Electroencephalograhie EEG, P300, Apprentissage profond, Potentiel évoqué cognitif PEC, Analyse des signaux, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.2: User Interfaces, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.1: Signal processing, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO]Cognitive science
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10
Authors: et al.
Contributors: et al.
Source: NeurIPS 2022 - 36th Conference on Neural Information Processing Systems ; https://inria.hal.science/hal-03807016 ; NeurIPS 2022 - 36th Conference on Neural Information Processing Systems, Nov 2022, New Orleans, United States
Subject Terms: Video Understanding, Video Question Answering, Zero-Shot Learning, Computer Vision, Vision and Language, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.10: Vision and Scene Understanding, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models, [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Subject Geographic: New Orleans, United States
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11
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Source: Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. 20th International Conference, PAAMS 2022, L'Aquila, Italy, July 13–15, 2022, Proceedings ; 20th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2022) ; https://ut3-toulouseinp.hal.science/hal-03776374 ; 20th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2022), Jul 2022, L'Aquila, Italy. pp.166-178, ⟨10.1007/978-3-031-18192-4_14⟩ ; https://edition2022.paams.net/
Subject Terms: Adaptative Multi-Agent System, Ensemble learning, Nonlinear classification, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.11: Distributed Artificial Intelligence, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.11: Distributed Artificial Intelligence/I.2.11.3: Multiagent systems, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
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12
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Source: à paraître ; 14th ITS European Congress (ITS EU 2022) ; https://ut3-toulouseinp.hal.science/hal-03776269 ; 14th ITS European Congress (ITS EU 2022), May 2022, Toulouse, France. pp.1-12 ; https://2022.itseuropeancongress.com/
Subject Terms: ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.11: Distributed Artificial Intelligence/I.2.11.3: Multiagent systems, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.2: Design Methodology/I.5.2.0: Classifier design and evaluation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
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13
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Source: SANER 2021 - 28th IEEE International Conference on Software Analysis, Evolution and Reengineering ; https://hal.science/hal-03447628 ; SANER 2021 - 28th IEEE International Conference on Software Analysis, Evolution and Reengineering, Mar 2021, Honolulu / Virtual, United States. pp.131-141, ⟨10.1109/SANER50967.2021.00021⟩ ; https://saner2021.shidler.hawaii.edu
Subject Terms: binary code analysis, graph neural networks, toolchain provenance, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.7: Distribution, Maintenance, and Enhancement/D.2.7.5: Restructuring, reverse engineering, and reengineering, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.5: Testing and Debugging/D.2.5.2: Diagnostics, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Subject Geographic: Honolulu / Virtual, United States
Relation: info:eu-repo/grantAgreement//830927/EU/Cyber security cOmpeteNCe fOr Research anD InnovAtion/CONCORDIA
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14
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Source: MLPA 2020 - Machine Learning for Program Analysis ; https://hal.science/hal-03270335 ; MLPA 2020 - Machine Learning for Program Analysis, Jan 2021, Yokohama / Virtual, Japan ; https://sites.google.com/view/mlpa2020
Subject Terms: toolchain provenance, machine learning, reverse engineering, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.7: Distribution, Maintenance, and Enhancement/D.2.7.5: Restructuring, and reengineering, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.5: Testing and Debugging/D.2.5.2: Diagnostics, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Subject Geographic: Yokohama / Virtual, Japan
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15
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Source: 13th International Joint Conference on Computational Intelligence
https://inria.hal.science/hal-03359384
13th International Joint Conference on Computational Intelligence, Oct 2021, Valletta, MaltaSubject Terms: Explicit Memory, Episodic Memory, Prospective Memory, Hippocampus, Prefrontal Cortex, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning/I.2.6.2: Connectionism and neural nets, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
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16
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Source: IEEE ICCI*CC'21 - 20th IEEE International Conference on Cognitive Informatics and Cognitive Computing ; https://inria.hal.science/hal-03359407 ; IEEE ICCI*CC'21 - 20th IEEE International Conference on Cognitive Informatics and Cognitive Computing, Oct 2021, Banff, Canada
Subject Terms: Creativity, Explicit memory, Episodic memory, Prospective memory, Cognitive control, Imagination, Computational cognitive neuroscience, Artificial intelligence, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [SCCO.NEUR]Cognitive science/Neuroscience
Subject Geographic: Banff
Time: Banff, Canada
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17
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Source: LNCS ; 25th European Conference on Advances in Databases and Information Systems (ADBIS 2021) ; https://hal.science/hal-03224965 ; 25th European Conference on Advances in Databases and Information Systems (ADBIS 2021), Aug 2021, Tartu, Estonia. pp.73-87, ⟨10.1007/978-3-030-82472-3_7⟩ ; https://adbis2021.cs.ut.ee/
Subject Terms: Social networks, Rumor verification, Image features, Machine learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Time: Tartu, Estonia
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2109.02271; ARXIV: 2109.02271
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18
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Source: LNCS ; Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2021) ; https://hal.science/hal-03265069 ; Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2021), Sep 2021, Bilbao, Spain. pp.497-513, ⟨10.1007/978-3-030-86517-7_31⟩ ; https://2021.ecmlpkdd.org/
Subject Terms: Social networks, Rumor detection, Deep neural networks, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Relation: info:eu-repo/semantics/altIdentifier/arxiv/2110.15727; ARXIV: 2110.15727
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19
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Source: https://hal.science/hal-03222107 ; Editions universitaires d'Avignon, 2021, 978-2-35768-127-9 ; https://eua.hypotheses.org/5167.
Subject Terms: ACM: D.: Software, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.11: Distributed Artificial Intelligence, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, ACM: I.: Computing Methodologies/I.7: DOCUMENT AND TEXT PROCESSING, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-GL]Computer Science [cs]/General Literature [cs.GL], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
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20
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Source: Khamassi, M. (Ed.) Neurosciences cognitives ; https://hal.science/hal-03411274 ; Khamassi, M. (Ed.) Neurosciences cognitives, 2021
Subject Terms: ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SCCO]Cognitive science, [SHS.PSY]Humanities and Social Sciences/Psychology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
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