Search Results - ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models/I.5.1.3: Neural net

MV-Net: Toward Real-Time Deep Learning on Mobile GPGPU Systems.

Authors: YIBIN TANG YING WANG HUAWEI LI et al.

Source: ACM Journal on Emerging Technologies in Computing Systems; Oct2019, Vol. 15 Issue 4, p1-25, 25p

Subject Terms: ARTIFICIAL neural networks, DEEP learning, MOBILE learning, RECURRENT neural networks, COMPUTER multitasking, MOBILE computing

Mathematics-Inspired Models: A Green and Interpretable Learning Paradigm for Multimedia Computing.

Authors: Gao, Lei Liu, Kai Guo, Zheng et al.

Source: ACM Transactions on Multimedia Computing, Communications & Applications; Jul2025, Vol. 21 Issue 7, p1-22, 22p

Subject Terms: ARTIFICIAL neural networks, MACHINE learning, MULTIMEDIA systems, ARTIFICIAL intelligence, SUSPICION

Quantization of Deep Neural Networks for Accurate Edge Computing.

Authors: WENTAO CHEN HAILONG QIU JIAN ZHUANG et al.

Source: ACM Journal on Emerging Technologies in Computing Systems; Oct2021, Vol. 17 Issue 4, p1-11, 11p

Subject Terms: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, RECURRENT neural networks, AUTOMATIC speech recognition, EDGE computing, IMAGE recognition (Computer vision), IMAGE segmentation

Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision.

Authors: Luo, Xiangzhong Liu, Di Kong, Hao et al.

Source: ACM Transactions on Embedded Computing Systems; Jan2025, Vol. 24 Issue 1, p1-100, 100p

Subject Terms: ARTIFICIAL neural networks, LANGUAGE models, MACHINE learning, TRANSFORMER models, IMAGE recognition (Computer vision), DEEP learning

Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.

Authors: Hossen, Md Amran Abas, Pg Emeroylariffion

Source: Journal of Imaging; Mar2025, Vol. 11 Issue 3, p91, 56p

Subject Terms: REINFORCEMENT learning, REAL-time computing, DEEP learning, RECURRENT neural networks, MACHINE learning, HUMAN activity recognition, SUPERVISED learning

Machine Learning with Confidential Computing: A Systematization of Knowledge.

Authors: Mo, Fan Tarkhani, Zahra Haddadi, Hamed

Source: ACM Computing Surveys; Nov2024, Vol. 56 Issue 11, p1-40, 40p

Subject Terms: ARTIFICIAL neural networks, MACHINE learning, PATTERN recognition systems, FEDERATED learning, SUPERVISED learning, DEEP learning

3D Brain and Heart Volume Generative Models: A Survey.

Authors: Liu, Yanbin Dwivedi, Girish Boussaid, Farid et al.

Source: ACM Computing Surveys; Jun2024, Vol. 56 Issue 6, p1-37, 37p

Subject Terms: ARTIFICIAL neural networks, PATTERN recognition systems, ARTIFICIAL intelligence, NATURAL language processing, MACHINE learning, IMAGE registration, IMAGE segmentation

Street-crimes modelled arms recognition technique employing deep learning and quantum deep learning (SMARTED).

Authors: Ali Shah, Syed Atif Abdel-Wahab, Ahmed Ageelani, Nasir et al.

Source: Indonesian Journal of Electrical Engineering & Computer Science; Apr2023, Vol. 30 Issue 1, p528-544, 17p

Subject Terms: OBJECT recognition (Computer vision), CONVOLUTIONAL neural networks, QUANTUM computing, REAL-time computing, CLOSED-circuit television, DEEP learning

NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge.

Authors: Xueyu Hou Yongjie Guan Tao Han

Source: MobiCom: International Conference on Mobile Computing & Networking; 2022, p186-199, 14p

Subject Terms: CONVOLUTIONAL neural networks, OBJECT recognition (Computer vision), MOBILE computing, MOBILE operating systems, COMPUTING platforms, COMPUTER systems, PHYSIOLOGICAL effects of acceleration

Geomorphology of the Puerto Rico Trench and Cayman Trough in the Context of the Geological Evolution of the Caribbean Sea ; Geomorfologia Rowu Puerto Rico i Rowu Kajmańskiego w kontekście ewolucji geologicznej Morza Karaibskiego

Authors: Lemenkova, Polina Ocean University of China (OUC)

Contributors: Lemenkova, Polina Ocean University of China (OUC)

Source: ISSN: 0137-1983 ; Annales Universitatis Mariae Curie-Sklodowska, sectio B – Geographia, Geologia, Mineralogia et Petrographia ; https://hal.archives-ouvertes.fr/hal-03022505 ; Annales Universitatis Mariae Curie-Sklodowska, sectio B – Geographia, Geologia, Mineralogia et Petrographia, 2020, 75, pp.115-141. ⟨10.17951/b.2020.75.115-141⟩ ; https://journals.umcs.pl/b/article/view/11219.

Subject Terms: Earth gravitational model, gravity anomalies, GMT, Cayman Trough, Puerto Rico Trench, Caribbean Sea, ACM: H.: Information Systems, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models/I.5.1.5: Structural, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.0: Computer vision, ACM: I.: Computing Methodologies, ACM: K.: Computing Milieux, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION, ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General, [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [SDE]Environmental Sciences, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDU]Sciences of the Universe [physics], [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences

Relation: hal-03022505; https://hal.archives-ouvertes.fr/hal-03022505; https://hal.archives-ouvertes.fr/hal-03022505/document; https://hal.archives-ouvertes.fr/hal-03022505/file/11219-46002-1-PB.pdf

Availability: https://hal.archives-ouvertes.fr/hal-03022505
https://hal.archives-ouvertes.fr/hal-03022505/document
https://hal.archives-ouvertes.fr/hal-03022505/file/11219-46002-1-PB.pdf
https://doi.org/10.17951/b.2020.75.115-141

Submarine tectonic geomorphology of the Pliny and Hellenic Trenches reflecting geologic evolution of the southern Greece ; Podmorska tektonska geomorfologija Plinijskoga i Helenskoga jarka kao odraz geološke evolucije južne Grčke

Authors: Lemenkova, Polina Schmidt United Institute of Physics of the Earth Moscow (IPE) Russian Academy of Sciences Moscow (RAS)

Contributors: Lemenkova, Polina Schmidt United Institute of Physics of the Earth Moscow (IPE) Russian Academy of Sciences Moscow (RAS)

Source: ISSN: 0353-4529 ; Rudarsko Geolosko Naftni Zbornik (The Mining-Geology-Petroleum Engineering Bulletin) ; https://hal.archives-ouvertes.fr/hal-03349895 ; Rudarsko Geolosko Naftni Zbornik (The Mining-Geology-Petroleum Engineering Bulletin), University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2021, 36 (4), pp.33-48. ⟨10.17794/rgn.2021.4.4⟩ ; 10.17794/rgn.2021.4.4.

Subject Terms: geophysics, generic mapping tools, Hellenic Trench, cartography, geology, ACM: H.: Information Systems, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES, ACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS, ACM: I.: Computing Methodologies, ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, [SDU.STU.TE]Sciences of the Universe [physics]/Earth Sciences/Tectonics

Relation: hal-03349895; https://hal.archives-ouvertes.fr/hal-03349895; https://hal.archives-ouvertes.fr/hal-03349895/document; https://hal.archives-ouvertes.fr/hal-03349895/file/16999-Article%20Text-68225-1-10-20210920.pdf

Availability: https://hal.archives-ouvertes.fr/hal-03349895
https://hal.archives-ouvertes.fr/hal-03349895/document
https://hal.archives-ouvertes.fr/hal-03349895/file/16999-Article%20Text-68225-1-10-20210920.pdf
https://doi.org/10.17794/rgn.2021.4.4

Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.

Authors: Shobayo, Olamilekan Saatchi, Reza

Source: Diagnostics (2075-4418); May2025, Vol. 15 Issue 9, p1072, 31p

Subject Terms: ARTIFICIAL neural networks, PATTERN recognition systems, ARTIFICIAL intelligence, TRANSFORMER models, GENERATIVE adversarial networks

Appearance and Pose-guided Human Generation: A Survey.

Authors: Liao, Fangjian Zou, Xingxing Wong, Waikeung

Source: ACM Computing Surveys; May2024, Vol. 56 Issue 5, p1-35, 35p

Subject Terms: ARTIFICIAL neural networks, PATTERN recognition systems, SUPERVISED learning, GENERATIVE adversarial networks, TRANSFORMER models, DEEP learning, POSE estimation (Computer vision), MIRROR neurons

HLAE-Net: A Hierarchical Lightweight Attention-Enhanced Strategy for Remote Sensing Scene Image Classification.

Authors: Yang, Mingyuan Shi, Cuiping Tan, Kangning et al.

Source: Remote Sensing; Oct2025, Vol. 17 Issue 19, p3279, 26p

Subject Terms: REMOTE sensing, IMAGE recognition (Computer vision), CONVOLUTIONAL neural networks, FEATURE extraction, MATHEMATICAL optimization

Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning.

Authors: Sponner, Max Waschneck, Bernd Kumar, Akash

Source: ACM Computing Surveys; Oct2024, Vol. 56 Issue 10, p1-40, 40p

Subject Terms: ARTIFICIAL neural networks, PATTERN recognition systems, REINFORCEMENT learning, DEEP reinforcement learning, CONVOLUTIONAL neural networks, DEEP learning

Distributed Graph Neural Network Training: A Survey.

Authors: Shao, Yingxia Li, Hongzheng Gu, Xizhi et al.

Source: ACM Computing Surveys; Aug2024, Vol. 56 Issue 8, p1-39, 39p

Subject Terms: GRAPH neural networks, ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MACHINE learning, PATTERN recognition systems, DEEP learning

Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks.

Authors: Yan, Jiangting Pu, Pengju Jiang, Liheng

Source: PLoS ONE; 3/3/2025, Vol. 20 Issue 3, p1-27, 27p

Subject Terms: MACHINE learning, GRAPH neural networks, EMOTION recognition, AFFECTIVE forecasting (Psychology), RANDOM fields, DEEP learning

Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks.

Authors: DAGHERO, FRANCESCO BURRELLO, ALESSIO CHEN XI et al.

Source: ACM Transactions on Embedded Computing Systems; Aug2022, Vol. 21 Issue 4, p1-28, 28p

Subject Terms: ARTIFICIAL neural networks, HUMAN activity recognition, DEEP learning, MICROCONTROLLERS, CONVOLUTIONAL neural networks, MACHINE learning

Early-Exit Deep Neural Network - A Comprehensive Survey.

Authors: Rahmath P, Haseena Srivastava, Vishal Chaurasia, Kuldeep et al.

Source: ACM Computing Surveys; Mar2025, Vol. 57 Issue 3, p1-37, 37p

Subject Terms: ARTIFICIAL intelligence, ARTIFICIAL neural networks, PATTERN recognition systems, GRAPH neural networks, NATURAL language processing, DEEP learning

Multi-Guidance CNNs for Salient Object Detection.

Authors: SHUAIXIONG HUI QIANG GUO XIAOYU GENG et al.

Source: ACM Transactions on Multimedia Computing, Communications & Applications; May2023, Vol. 19 Issue 3, p1-19, 19p

Subject Terms: OBJECT recognition (Computer vision), CONVOLUTIONAL neural networks