Efficient annotation reduction with active learning for computer vision-based Retail Product Recognition

Authors: Griffioen, Niels, Rankovic, Nevena, Zamberlan, Federico, Punith, Monisha

Source: Journal of computational social science

Subject Terms: Artificial intelligence, Active learning, 4609 Information systems, Semi-Supervised Learning, Annotation, Social Sciences, Active Learning, 02 engineering and technology, International trade, Fabric Defect Detection in Industrial Applications, Quantum mechanics, 7. Clean energy, Annotation effort, Industrial and Manufacturing Engineering, Learning with Noisy Labels in Machine Learning, 12. Responsible consumption, Engineering, Deep Learning, Sociology, Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Entropy (arrow of time), Business, Retail image analysis, Active Learning in Machine Learning Research, 4701 Communication and media studies, Physics, 4. Education, Retraining, 4410 Sociology, Social Sciences, Mathematical Methods, Active learning (machine learning), 15. Life on land, Computer science, Algorithm, Computer Science, Physical Sciences, Instance segmentation, Mathematics, Mathematical Methods In Social Sciences, Robust Learning

Access URL: https://research.tilburguniversity.edu/en/publications/544d20d5-2b8e-4483-b9dd-b2be4d161c17
https://doi.org/10.1007/s42001-024-00266-7
https://lirias.kuleuven.be/handle/20.500.12942/744482
https://doi.org/10.1007/s42001-024-00266-7
https://repository.uantwerpen.be/docstore/d:irua:23253
https://hdl.handle.net/10067/2054360151162165141

Universal consistency of the k-NN rule in metric spaces and Nagata dimension. II

Authors: Sushma Kumari, Vladimir Pestov

Source: ESAIM: Probability and Statistics. 28:132-160

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Metric (unit), Learning and Inference in Bayesian Networks, Economics, Separable space, Space (punctuation), Mathematical analysis, Metric Spaces, Machine Learning (cs.LG), Computational Complexity and Algorithmic Information Theory, Artificial Intelligence, Euclidean space, FOS: Mathematics, Active Learning in Machine Learning Research, Pure mathematics, Discrete mathematics, Minkowski–Bouligand dimension, Computer science, Packing dimension, Operating system, Dimension (graph theory), Operations management, Computational Theory and Mathematics, Computer Science, Physical Sciences, Metric space, Fractal dimension, Fractal, 62H30, 54F45, Mathematics, Consistency (knowledge bases)

Access URL: http://arxiv.org/abs/2305.17282

Language Outlay for the Sokoto Cement Production System Using Deterministic Finite Automata Scheme

Authors: Zaid Ibrahim

Source: European Journal of Theoretical and Applied Sciences. 1:105-114

Subject Terms: Algebraic number, Automaton, Economics, Automata Theory and Formal Languages, Macroeconomics, 0211 other engineering and technologies, 02 engineering and technology, Mathematical analysis, 12. Responsible consumption, Theoretical computer science, Artificial Intelligence, 11. Sustainability, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Production (economics), Active Learning in Machine Learning Research, Scheme (mathematics), Algebra over a field, Arithmetic, Text Compression and Indexing Algorithms, 4. Education, Formal Languages, Pure mathematics, Semantics (computer science), Discrete mathematics, 15. Life on land, Substitution (logic), Computer science, Process (computing), Programming language, Computational Theory and Mathematics, 13. Climate action, Computer Science, Physical Sciences, 8. Economic growth, Binary number, Mathematics

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions

Authors: Bhattamishra, S, Patel, A, Kanade, V, Blunsom, P

Source: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). :5767-5791

Subject Terms: Artificial neural network, FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Economics, Semi-Supervised Learning, Generalization, Overfitting, 02 engineering and technology, Mathematical analysis, 01 natural sciences, Machine Learning (cs.LG), Task (project management), Engineering, Artificial Intelligence, Boolean function, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Natural Language Processing, 0105 earth and related environmental sciences, Transformer, Computer Science - Computation and Language, Inductive bias, Text Compression and Indexing Algorithms, Voltage, Computer science, Management, Algorithm, Multi-task learning, Electrical engineering, Computer Science, Physical Sciences, Computation and Language (cs.CL), Mathematics

Access URL: http://arxiv.org/abs/2211.12316
https://ora.ox.ac.uk/objects/uuid:9c4af441-8435-4767-b0eb-dec15bce2ce5
https://doi.org/10.18653/v1/2023.acl-long.317

Fast and Informative Model Selection Using Learning Curve Cross-Validation

Authors: Felix Mohr, Jan N. van Rijn

Source: IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:9669-9680

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Semi-Supervised Learning, Handling Imbalanced Data in Classification Problems, Active Learning, 02 engineering and technology, Model selection, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Context (archaeology), Selection (genetic algorithm), Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Data mining, Biology, Cost-Sensitive Learning, Paleontology, Cross-validation, Computer science, Process (computing), Operating system, Computer Science, Physical Sciences, Algorithms, Learning Curve, Robust Learning, Labeled data, Training set

Access URL: https://pubmed.ncbi.nlm.nih.gov/37028368
http://arxiv.org/abs/2111.13914

Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator

Authors: Haobo Qi, Feifei Wang, Hansheng Wang

Source: Journal of Computational and Graphical Statistics. 32:1348-1360

Subject Terms: FOS: Computer and information sciences, Artificial neural network, Artificial intelligence, Economics, Computational Mechanics, Sample size determination, Statistics - Computation, Estimator, Mathematical analysis, Engineering, Artificial Intelligence, FOS: Mathematics, Optimization Methods in Machine Learning, Large-Scale Optimization, Active Learning in Machine Learning Research, Computation (stat.CO), Economic growth, Stochastic Gradient Descent, Computer network, Gradient descent, Mathematical optimization, Statistics, Fixed point, Rate of convergence, Theory and Applications of Compressed Sensing, Applied mathematics, Computer science, Algorithm, Channel (broadcasting), Computer Science, Physical Sciences, Computation, Convergence (economics), Mathematics

Access URL: http://arxiv.org/abs/2304.06564

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Authors: Erik Orm Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjølstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi

Source: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4. :19-42

Subject Terms: FOS: Computer and information sciences, Parallel computing, Computer Science - Machine Learning, Artificial intelligence, Hyperparameter Optimization, Compiler, 02 engineering and technology, Bayesian probability, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Parallel Computing and Performance Optimization, GPU Computing, Computer architecture, Active Learning in Machine Learning Research, Bayesian optimization, Computer Science - Programming Languages, Computer Science - Performance, Performance Optimization, Computer science, Programming language, Performance (cs.PF), Hardware and Architecture, Optimizing compiler, Computer Science, Physical Sciences, Programming Languages (cs.PL)

Access URL: http://arxiv.org/abs/2212.11142

A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning

Authors: Dina Elreedy, Amir F. Atiya, Firuz Kamalov

Source: Machine Learning. 113:4903-4923

Subject Terms: Artificial intelligence, Class (philosophy), Conditional probability distribution, Semi-Supervised Learning, Handling Imbalanced Data in Classification Problems, 02 engineering and technology, Pattern recognition (psychology), Mathematical analysis, Bandwidth (computing), Engineering, Artificial Intelligence, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Ensemble Methods, Active Learning in Machine Learning Research, SMOTE, Computer network, Imbalanced Data, Distribution (mathematics), Electricity Theft Detection in Smart Grids, Motion (physics), 4. Education, Oversampling, Statistics, Computer science, Algorithm, Computer Science, Physical Sciences, Interpolation (computer graphics), Mathematics

Naive automated machine learning

Authors: Felix Mohr, Marcel Wever

Source: Machine Learning. 112:1131-1170

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Hyperparameter Optimization, Generalization, 02 engineering and technology, Mathematical analysis, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Artificial Intelligence, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Swarm Intelligence Optimization Algorithms, Active Learning in Machine Learning Research, Constraint Handling, Global Optimization, Optimization Applications, Computer science, Programming language, Automated Machine Learning, Algorithm, Combinatorics, Computer Science, Physical Sciences, Tree (set theory), Pipeline (software), Mathematics

Access URL: http://arxiv.org/abs/2111.14514
https://epub.ub.uni-muenchen.de/107520/

Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

Authors: Fernando Marmolejo‐Ramos, Raydonal Ospina, Enrique García-Ceja, Juan Carlos Correa

Contributors: Marmolejo-Ramos, Fernando, Ospina, Raydonal, García-Ceja, Enrique, Correa, Juan C

Source: J Stat Theory Appl

Subject Terms: 0301 basic medicine, Artificial intelligence, causality, Explainable Artificial Intelligence, Review, 01 natural sciences, Learning with Noisy Labels in Machine Learning, 03 medical and health sciences, Inference, Artificial Intelligence, Computer security, Machine learning, Interpretability, 0101 mathematics, Model Interpretability, Key (lock), Active Learning in Machine Learning Research, Visualization, inference, predictive statistics, Responsibility in AI, Computer science, Machine Learning Interpretability, Process (computing), Programming language, machine learning, Computer Science, Physical Sciences, Interpretable Models, Robust Learning, Workbench

Access URL: https://pubmed.ncbi.nlm.nih.gov/36160758
https://hdl.handle.net/11541.2/31277

Classification in Multi-Label Datasets

Authors: Aouatef, Mahani

Source: Information Systems Management ISBN: 9781803556512
Information Systems Management

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Class (philosophy), Economics, Multi-label classification, Pattern recognition (psychology), Computer science, Detection and Prevention of Phishing Attacks, Management, Task (project management), Artificial Intelligence, Categorization, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Machine learning, Text categorization, Multi-label Learning, Active Learning in Machine Learning Research, Data mining, Information Systems

Access URL: https://mts.intechopen.com/articles/show/title/classification-in-multi-label-datasets

Mitigating Machine Learning Bias Between High Income and Low-Middle Income Countries for Enhanced Model Fairness and Generalizability

Authors: Jenny Yang, Lei Clifton, Nguyen Thanh Dung, Nguyễn Thanh Phong, Lam Minh Yen, Doan Bui Xuan Thy, Andrew A. S. Soltan, Louise Thwaites, David A. Clifton

Source: Sci Rep
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)

Subject Terms: Pooling, Artificial intelligence, Explainable Artificial Intelligence, Developing country, Economics, Developmental psychology, Semi-Supervised Learning, Low and middle income countries, FOS: Political science, Social Sciences, Machine Learning, Psychology, Equity (law), Business, Model Interpretability, 10. No inequality, Political science, Algorithmic Bias, 1. No poverty, FOS: Philosophy, ethics and religion, 3. Good health, FOS: Psychology, Vietnam, Physical Sciences, 8. Economic growth, Income, Medicine, Interpretable Models, Safety Research, Algorithms, Science, Generalizability theory, Health Informatics, Vietnamese, FOS: Law, Article, FOS: Economics and business, Bias, Artificial Intelligence, Capability Approach, Health Sciences, Machine learning, Humans, Econometrics, Developing Countries, Active Learning in Machine Learning Research, Economic growth, Artificial Intelligence in Medicine, SARS-CoV-2, Developed Countries, Ethical Implications of Artificial Intelligence, COVID-19, Linguistics, Computer science, United Kingdom, Philosophy, Computer Science, FOS: Languages and literature, Law

Access URL: https://pubmed.ncbi.nlm.nih.gov/38858466
https://doaj.org/article/a88db8c825944c868781efc98703caa4

Quantifying the impact of uninformative features on the performance of supervised classification and dimensionality reduction algorithms

Authors: Weihua Lei, Cleber Zanchettin, Zoey E. Ho, Luı́s A. Nunes Amaral

Source: APL Machine Learning, Vol 1, Iss 4, Pp 046118-046118-10 (2023)

Subject Terms: 0301 basic medicine, Artificial intelligence, QC1-999, Semi-Supervised Learning, Robustness (evolution), Handling Imbalanced Data in Classification Problems, 02 engineering and technology, Pattern recognition (psychology), Biochemistry, Gene, Learning with Noisy Labels in Machine Learning, 03 medical and health sciences, Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Data mining, Curse of dimensionality, Cost-Sensitive Learning, Imbalanced Data, Physics, 4. Education, QA75.5-76.95, 15. Life on land, Computer science, Dimensionality reduction, Algorithm, Chemistry, Electronic computers. Computer science, Computer Science, Physical Sciences, Feature selection, Robust Learning

Access URL: https://doaj.org/article/46de7007ca4e4133a547b5f336315b06

Embrace open-environment machine learning for robust AI

Authors: Gang� Li, Aswani Kumar Cherukuri

Source: Natl Sci Rev

Subject Terms: Artificial intelligence, Active Learning, Transformative learning, 02 engineering and technology, Learning with Noisy Labels in Machine Learning, Artificial Intelligence, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Active Learning in Machine Learning Research, Adaptation to Concept Drift in Data Streams, Biology, Ecology, Special Topic: Machine Learning Automation, 9. Industry and infrastructure, Pedagogy, 4. Education, 15. Life on land, Computer science, Adaptability, FOS: Psychology, 13. Climate action, FOS: Biological sciences, Computer Science, Physical Sciences, Robust Learning

Access URL: https://pubmed.ncbi.nlm.nih.gov/39007001

Maximizing reusability of learning objects through machine learning techniques

Authors: Meryem Amane, Mounir Gouiouez, Mohammed Berrada

Source: Sci Rep
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)

Subject Terms: Structuring, Artificial intelligence, Economics, Science, Semi-Supervised Learning, Article, Learning with Noisy Labels in Machine Learning, Task (project management), Artificial Intelligence, Meta-Learning, Machine learning, Image (mathematics), Similarity (geometry), Active Learning in Machine Learning Research, Adaptation to Concept Drift in Data Streams, Ensemble Learning, Reusability, Curse of dimensionality, Metadata, Instance-based learning, Active learning (machine learning), Computer science, Management, Programming language, Meta learning (computer science), World Wide Web, Online Learning, Categorization, Computer Science, Physical Sciences, Medicine, Learning object, Software, Finance, Robust Learning

Access URL: https://pubmed.ncbi.nlm.nih.gov/37821482
https://doaj.org/article/8b653bb00faa4b938bbcd5ee1ea792a6

Estimation of the Conditional Probability Using a Stochastic Gradient Process

Authors: Ali Labriji, Abdelkrim Bennar, Mostafa Rachik

Source: Journal of Mathematics, Vol 2021 (2021)

Subject Terms: Statistics and Probability, 0301 basic medicine, Volume (thermodynamics), Economics, Conditional probability, Geometry, Regularization and Variable Selection Methods, Social psychology, Quantum mechanics, Gaussian Processes in Machine Learning, FOS: Economics and business, 03 medical and health sciences, Point (geometry), Artificial Intelligence, QA1-939, FOS: Mathematics, Psychology, Econometrics, Active Learning in Machine Learning Research, Data mining, Probabilistic Models, Point process, Economic growth, Conditional expectation, 0303 health sciences, Physics, Mathematical optimization, Statistics, Computer science, Process (computing), Management, Algorithm, FOS: Psychology, Popularity, Operating system, Computer Science, Physical Sciences, Convergence (economics), Covariance Estimation, Estimation, Mathematics

File Description: text/xhtml

Access URL: https://downloads.hindawi.com/journals/jmath/2021/7660113.pdf
https://doaj.org/article/7fc7d8904f0c48e6a85226bca384a9d2

Efficient traversal of decision tree ensembles with FPGAs

Authors: Romina Soledad Molina, Veronica Gil-Costa, Franco Maria Nardini, Fernando Loor, Salvatore Trani, Raffaele Perego

Contributors: Molina, R., Loor, F., Gil-Costa, V., Nardini, F. M., Perego, R., Trani, S.

Source: Journal of parallel and distributed computing
155 (2021): 38–49. doi:10.1016/j.jpdc.2021.04.008
info:cnr-pdr/source/autori:Molina R.; Loor F.; Gil-Costa V.; Nardini F.M.; Perego R.; Trani S./titolo:Efficient traversal of decision tree ensembles with FPGAs/doi:10.1016%2Fj.jpdc.2021.04.008/rivista:Journal of parallel and distributed computing (Print)/anno:2021/pagina_da:38/pagina_a:49/intervallo_pagine:38–49/volume:155

Subject Terms: System on Chip, Artificial intelligence, Explainable Artificial Intelligence, Acceleration, 02 engineering and technology, Latency (audio), Learning with Noisy Labels in Machine Learning, Decision trees, FPGA, Machine learning, Inference, Hardware acceleration, Artificial Intelligence, Computer engineering, Decision tree, 0202 electrical engineering, electronic engineering, information engineering, Software deployment, Computer architecture, Classical mechanics, Embedded system, Active Learning in Machine Learning Research, DECISION TREES, System on a chip, Physics, Scalability, Computer science, Field-programmable gate array, Tree traversal, Algorithm, Operating system, Computer Science, Physical Sciences, Telecommunications, Ranking, MACHINE LEARNING, SYSTEM ON CHIP, Robust Learning

File Description: ELETTRONICO; application/pdf

Access URL: https://www.sciencedirect.com/science/article/pii/S0743731521000915
https://hdl.handle.net/11368/3034418
http://hdl.handle.net/11336/150230
https://www.sciencedirect.com/science/article/pii/S0743731521000915
https://doi.org/10.1016/j.jpdc.2021.04.008
https://hdl.handle.net/11368/3034418

AutoML for Multi-Label Classification: Overview and Empirical Evaluation

Authors: Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hullermeier

Source: IEEE Transactions on Pattern Analysis and Machine Intelligence. 43:3037-3054

Subject Terms: Marketing, Artificial intelligence, History, Hyperparameter Optimization, Semi-Supervised Learning, 02 engineering and technology, Computer science, Learning with Noisy Labels in Machine Learning, Automated Machine Learning, FOS: Economics and business, Machine Learning Algorithms, Benchmarking, Archaeology, Artificial Intelligence, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Suite, Business, Multi-label Learning, Active Learning in Machine Learning Research

Access URL: https://ieeexplore.ieee.org/ielx7/34/9506969/09321731.pdf
https://pubmed.ncbi.nlm.nih.gov/33439834
https://www.ncbi.nlm.nih.gov/pubmed/33439834
https://www.computer.org/csdl/journal/tp/2021/09/09321731/1qmbhpPOIp2
https://ieeexplore.ieee.org/document/9321731
https://ris.uni-paderborn.de/publication/21004
https://dblp.uni-trier.de/db/journals/pami/pami43.html#WeverTMH21
https://epub.ub.uni-muenchen.de/91545/

Dynamic Knowledge Distillation for Pre-trained Language Models

Authors: Lei Li, Yankai Lin, Siming Ren, Peng Li, Jie Zhou, Xu Sun

Source: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. :379-389

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Scale (ratio), Semi-Supervised Learning, Active Learning, Organic chemistry, 02 engineering and technology, Quantum mechanics, 01 natural sciences, Machine Learning (cs.LG), Selection (genetic algorithm), Artificial Intelligence, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Natural Language Processing, Distillation, 0105 earth and related environmental sciences, Computer Science - Computation and Language, Adaptation (eye), Physics, 4. Education, Optics, Statistical Machine Translation and Natural Language Processing, Computer science, Language model, Language Modeling, Chemistry, Computer Science, Physical Sciences, Computation and Language (cs.CL)

Access URL: https://aclanthology.org/2021.emnlp-main.31.pdf
http://arxiv.org/abs/2109.11295

Secure Image Inference Using Pairwise Activation Functions

Authors: Jonas T. Agyepong, Mostafa I. Soliman, Yasutaka Wada, Keiji Kimura, Ahmed El-Mahdy

Source: IEEE Access, Vol 9, Pp 118271-118290 (2021)

Subject Terms: Artificial neural network, 0301 basic medicine, Artificial intelligence, Ciphertext, Exponentiation, Encryption, MNIST database, Mathematical analysis, Searchable Encryption, Activation function, 03 medical and health sciences, Theoretical computer science, Artificial Intelligence, FOS: Mathematics, Optimization Methods in Machine Learning, pairwise functions, Active Learning in Machine Learning Research, Pairing-based Cryptography, 0303 health sciences, Discrete logarithm, Advanced Cryptographic Schemes and Protocols, privacy-preserving machine learning, Public-key cryptography, Plaintext, Computer science, Homomorphic encryption, TK1-9971, Exploratory analysis, Algorithm, Operating system, homomorphic encryption scheme, Computer Science, Physical Sciences, polynomial approximation, Cryptography, Electrical engineering. Electronics. Nuclear engineering, Privacy-Preserving Computation, Homomorphic Encryption, Approximation Algorithms, homomorphic image inference, Mathematics

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09521226.pdf
https://doaj.org/article/f78bfa943e7f46b2a727bf8fc587c134
https://dblp.uni-trier.de/db/journals/access/access9.html#AgyepongSWKE21
https://doi.org/10.1109/ACCESS.2021.3106888
https://doaj.org/article/f78bfa943e7f46b2a727bf8fc587c134
https://ieeexplore.ieee.org/document/9521226/;