Suchergebnisse - "Computer Science - Machine Learning"

Counterfactual explanations for linear optimization

Autoren: Jannis Kurtz Ş. İlker Birbil Dick den Hertog

Quelle: European Journal of Operational Research. 329:24-41

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2405.15431

Soft regression trees: A model variant and a decomposition training algorithm

Autoren: Antonio Consolo Edoardo Amaldi Andrea Manno

Quelle: European Journal of Operational Research. 328:607-619

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Decomposition algorithms, Machine learning, Nonlinear programming, Regression trees, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control, Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2501.05942

Predicting space tourism demand using explainable AI

Autoren: Tan-Hanh Pham Jingchen Bi Rodrigo Mesa-Arango et al.

Quelle: Acta Astronautica. 238:1225-1237

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2503.03113

Optimizing treatment allocation in the presence of interference

Autoren: Daan Caljon Jente Van Belle Jeroen Berrevoets et al.

Quelle: European Journal of Operational Research. 328:620-632

Schlagwörter: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Social and Information Networks, Machine Learning (stat.ML), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2410.00075

Adaptive random Fourier features training stabilized by resampling with applications in image regression

Autoren: Kammonen, Aku Pandey, Anamika von Schwerin, Erik et al.

Quelle: Foundations of Data Science. 8:200-235

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2410.06399

Neural networks for threshold dynamics reconstruction

Autoren: Negrini, Elisa Gao, Almanzo Jiahe Bowering, Abigail et al.

Quelle: Inverse Problems and Imaging. 20:202-235

Schlagwörter: Computer Science - Machine Learning, Mathematics - Dynamical Systems, 68T07 (Primary), 65P99, 37M05 (Secondary)

Zugangs-URL: http://arxiv.org/abs/2412.09079

Text clustering with large language model embeddings

Autoren: Alina Petukhova João P. Matos-Carvalho Nuno Fachada et al.

Weitere Verfasser: Alina Petukhova João P. Matos-Carvalho Nuno Fachada et al.

Quelle: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 100-108 (2025)

Schlagwörter: I.7.m, FOS: Computer and information sciences, Computer Science - Machine Learning, Information Systems and Management, Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.6, Science, I.2.7, QA75.5-76.95, Computer Science Applications, Text summarisation, Machine Learning (cs.LG), Text clustering, Artificial Intelligence (cs.AI), Electronic computers. Computer science, Large language models, 68T50 (Primary), 62H30 (Secondary), Engineering (miscellaneous), Computation and Language (cs.CL), Information Systems

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2403.15112
https://doaj.org/article/0d07ddd5e301430f8c147693d73f9264
https://hdl.handle.net/10362/183810

A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network

Autoren: Kwanghyuk Park Xinjuan Chen Dongjin Lee et al.

Quelle: Applied Numerical Mathematics. 218:1-21

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Mathematics, Computer Science - Neural and Evolutionary Computing, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2407.06333

Application of machine learning and convex limiting to subgrid flux modeling in the shallow-water equations

Autoren: Ilya Timofeyev Alexey Schwarzmann Dmitri Kuzmin

Quelle: Mathematics and Computers in Simulation. 238:163-178

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics, Statistics - Machine Learning, 65M99, Atmospheric and Oceanic Physics (physics.ao-ph), Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Machine Learning (stat.ML), Physics - Fluid Dynamics, Computational Physics (physics.comp-ph), Physics - Computational Physics, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2407.17214

Scalarisation-based risk concepts for robust multi-objective optimisation

Autoren: Ben Tu Nikolas Kantas Robert M. Lee et al.

Quelle: European Journal of Operational Research. 327:559-576

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Machine Learning (stat.ML), Mathematics - Optimization and Control, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2405.10221

Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set

Autoren: Gordon Dai Pavan Ravishankar Rachel Yuan et al.

Quelle: Proceedings of the 5th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. :42-73

Schlagwörter: FOS: Computer and information sciences, Computer Science - Computers and Society, Computer Science - Machine Learning, Computers and Society (cs.CY), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2501.15634

Challenging the Validity of Personality Tests for Large Language Models

Autoren: Tom Sühr Florian E. Dorner Samira Samadi et al.

Quelle: Proceedings of the 5th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. :74-81

Schlagwörter: I.2, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), J.4, H.1, I.6, Computer Science - Artificial Intelligence, Computation and Language (cs.CL), 91E45, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2311.05297

Private Aggregation in Hierarchical Wireless Federated Learning With Partial and Full Collusion

Autoren: Maximilian Egger Christoph Hofmeister Antonia Wachter-Zeh et al.

Quelle: IEEE Transactions on Information Theory. 71:8977-8992

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning, Machine Learning (stat.ML), Distributed, Parallel, and Cluster Computing (cs.DC), Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2306.14088

Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting

Autoren: Farzan Kaviani Ivan Markovsky Hamid R. Ossareh

Quelle: IEEE Transactions on Automatic Control. 70:7588-7595

Schlagwörter: FOS: Computer and information sciences, Technology, Computer Science - Machine Learning, Noise measurement, uncertainty quantification, Trajectory, truncated singular value decomposition (TSVD) denoising, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Machine Learning (cs.LG), Predictive models, Matrix decomposition, Automation & Control Systems, Engineering, 0102 Applied Mathematics, output prediction error bounds, FOS: Electrical engineering, electronic engineering, information engineering, Noise reduction, Accuracy, Science & Technology, Data-driven control, PERTURBATION, 4007 Control engineering, mechatronics and robotics, Uncertainty, Engineering, Electrical & Electronic, Vectors, 0906 Electrical and Electronic Engineering, Industrial Engineering & Automation, Noise, Upper bound, 0913 Mechanical Engineering

Zugangs-URL: http://arxiv.org/abs/2404.15098
https://lirias.kuleuven.be/handle/20.500.12942/767081
https://doi.org/10.1109/tac.2025.3573151

Universal Rate-Distortion-Perception Representations for Lossy Compression

Autoren: George Zhang Jingjing Qian Jun Chen et al.

Quelle: IEEE Transactions on Information Theory. 71:8633-8653

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Information Theory, Information Theory (cs.IT), 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2106.10311

An Adaptive ML Framework for Power Converter Monitoring via Federated Transfer Learning

Autoren: Panagiotis Kakosimos Alireza Nemat Saberi Luca Peretti

Quelle: IEEE Transactions on Power Electronics. 40:16048-16054

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2504.16866

Similarity and Dissimilarity Guided Co-Association Matrix Construction for Ensemble Clustering

Autoren: Xu Zhang Yuheng Jia Mofei Song et al.

Quelle: IEEE Transactions on Knowledge and Data Engineering. 37:6694-6707

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2411.00904

SpeGCL: Self-Supervised Graph Spectrum Contrastive Learning Without Positive Samples

Autoren: Yuntao Shou Xiangyong Cao Deyu Meng

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:19546-19559

Schlagwörter: Computer Science - Machine Learning, Computer Science - Artificial Intelligence

Zugangs-URL: http://arxiv.org/abs/2410.10365

One Neuron Saved is One Neuron Earned: On Parametric Efficiency of Quadratic Networks

Autoren: Feng-Lei Fan Hang-Cheng Dong Zhongming Wu et al.

Quelle: IEEE Transactions on Pattern Analysis and Machine Intelligence. 47:9702-9717

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, 02 engineering and technology, Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2303.06316

EclipseNETs: Learning irregular small celestial body silhouettes

Autoren: Giacomo Acciarini Dario Izzo Francesco Biscani

Quelle: Acta Astronautica. 236:514-521

Schlagwörter: Earth and Planetary Astrophysics (astro-ph.EP), FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics - Earth and Planetary Astrophysics, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2504.04455