Relevant bibliographies by topics / Data-efficient Deep Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data-efficient Deep Learning'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Data-efficient Deep Learning"

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Dr., Sumit Chaudhary, Neha Singh Ms., and Pankaj Salaiya. "Time-Efficient Algorithm for Data Annotation using Deep Learning." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 2, no. 5 (2022): 8–11. https://doi.org/10.54105/ijainn.E1058.082522.

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<strong>Abstract:</strong> Current generation emphasis on the Digital world which creates a lot of unbeneficial data. The paper is about data annotation using deep learning as there is a lot of data available online but which data is useful can be labeled using these techniques. The unstructured data is labeled by many techniques but implementation of deep learning for labeling the unstructured data results in saving the time with high efficiency. In this paper introduce a method for data annotation, for that we can use unlabeled data as input and it is classified using the K-Nearest Neighbor
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Chaudhary, Dr Sumit, Ms Neha Singh, and Salaiya Pankaj. "Time-Efficient Algorithm for Data Annotation using Deep Learning." Indian Journal of Artificial Intelligence and Neural Networking 2, no. 5 (2022): 8–11. http://dx.doi.org/10.54105/ijainn.e1058.082522.

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Current generation emphasis on the Digital world which creates a lot of unbeneficial data. The paper is about data annotation using deep learning as there is a lot of data available online but which data is useful can be labeled using these techniques. The unstructured data is labeled by many techniques but implementation of deep learning for labeling the unstructured data results in saving the time with high efficiency. In this paper introduce a method for data annotation, for that we can use unlabeled data as input and it is classified using the K-Nearest Neighbor algorithm. K-Nearest Neighb
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Biswas, Surojit, Grigory Khimulya, Ethan C. Alley, Kevin M. Esvelt, and George M. Church. "Low-N protein engineering with data-efficient deep learning." Nature Methods 18, no. 4 (2021): 389–96. http://dx.doi.org/10.1038/s41592-021-01100-y.

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Edstrom, Jonathon, Yifu Gong, Dongliang Chen, Jinhui Wang, and Na Gong. "Data-Driven Intelligent Efficient Synaptic Storage for Deep Learning." IEEE Transactions on Circuits and Systems II: Express Briefs 64, no. 12 (2017): 1412–16. http://dx.doi.org/10.1109/tcsii.2017.2767900.

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Feng, Wenhui, Chongzhao Han, Feng Lian, and Xia Liu. "A Data-Efficient Training Method for Deep Reinforcement Learning." Electronics 11, no. 24 (2022): 4205. http://dx.doi.org/10.3390/electronics11244205.

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Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often prohibitive to take weeks to train an algorithm. In this study, a data-efficient training method is proposed in which a DQN is used as a base algorithm, and an elaborate curriculum is designed for the agent in the simulation scenario to accelerate the training process. In the early stage of the training process, the distribution of the initial state i
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Hu, Wenjin, Feng Liu, and Jiebo Peng. "An Efficient Data Classification Decision Based on Multimodel Deep Learning." Computational Intelligence and Neuroscience 2022 (May 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/7636705.

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A single model is often used to classify text data, but the generalization effect of a single model on text data sets is poor. To improve the model classification accuracy, a method is proposed that is based on a deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) and integrates multiple models trained by a deep learning network architecture to obtain a strong text classifier. Additionally, to increase the flexibility and accuracy of the model, various optimizer algorithms are used to train data sets. Moreover, to reduce the interference in the cla
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Mairittha, Nattaya, Tittaya Mairittha, and Sozo Inoue. "On-Device Deep Learning Inference for Efficient Activity Data Collection." Sensors 19, no. 15 (2019): 3434. http://dx.doi.org/10.3390/s19153434.

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Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition
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Dheepan, G. M. Karpura, Shaik Mohammed Rafee, Prasanthi Badugu, and Sunil Kumar. "A DEEP LEARNING TECHNIQUE FOR EFFICIENT MULTIMEDIA FOR DATA COMPRESSION." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3169–74. http://dx.doi.org/10.21917/ijivp.2024.0451.

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Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimi
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Duan, Yanjie, Yisheng Lv, Yu-Liang Liu, and Fei-Yue Wang. "An efficient realization of deep learning for traffic data imputation." Transportation Research Part C: Emerging Technologies 72 (November 2016): 168–81. http://dx.doi.org/10.1016/j.trc.2016.09.015.

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Sashank, Madipally Sai Krishna, Vijay Souri Maddila, Vikas Boddu, and Y. Radhika. "Efficient deep learning based data augmentation techniques for enhanced learning on inadequate medical imaging data." ACTA IMEKO 11, no. 1 (2022): 6. http://dx.doi.org/10.21014/acta_imeko.v11i1.1226.

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&lt;p class="Abstract"&gt;The world has come to a standstill with the Coronavirus taking over. In these dire times, there are fewer doctors and more patients and hence, treatment is becoming more and more difficult and expensive. In recent times, Computer Science, Machine Intelligence, measurement technology has made a lot of progress in the field of Medical Science hence aiding the automation of a lot of medical activities. One area of progress in this regard is the automation of the process of detection of respiratory diseases (such as COVID-19). There have been many Convolutional Neural Net
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Dissertations / Theses on the topic "Data-efficient Deep Learning"

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Lundström, Dennis. "Data-efficient Transfer Learning with Pre-trained Networks." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138612.

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Deep learning has dominated the computer vision field since 2012, but a common criticism of deep learning methods is their dependence on large amounts of data. To combat this criticism research into data-efficient deep learning is growing. The foremost success in data-efficient deep learning is transfer learning with networks pre-trained on the ImageNet dataset. Pre-trained networks have achieved state-of-the-art performance on many tasks. We consider the pre-trained network method for a new task where we have to collect the data. We hypothesize that the data efficiency of pre-trained networks
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Edstrom, Jonathon. "Embracing Visual Experience and Data Knowledge: Efficient Embedded Memory Design for Big Videos and Deep Learning." Diss., North Dakota State University, 2019. https://hdl.handle.net/10365/31558.

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Energy efficient memory designs are becoming increasingly important, especially for applications related to mobile video technology and machine learning. The growing popularity of smart phones, tablets and other mobile devices has created an exponential demand for video applications in today’s society. When mobile devices display video, the embedded video memory within the device consumes a large amount of the total system power. This issue has created the need to introduce power-quality tradeoff techniques for enabling good quality video output, while simultaneously enabling power consumption
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Sagen, Markus. "Large-Context Question Answering with Cross-Lingual Transfer." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440704.

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Models based around the transformer architecture have become one of the most prominent for solving a multitude of natural language processing (NLP)tasks since its introduction in 2017. However, much research related to the transformer model has focused primarily on achieving high performance and many problems remain unsolved. Two of the most prominent currently are the lack of high performing non-English pre-trained models, and the limited number of words most trained models can incorporate for their context. Solving these problems would make NLP models more suitable for real-world application
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Loiseau, Romain. "Real-World 3D Data Analysis : Toward Efficiency and Interpretability." Electronic Thesis or Diss., Marne-la-vallée, ENPC, 2023. http://www.theses.fr/2023ENPC0028.

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Cette thèse explore de nouvelles approches d'apprentissage profond pour l'analyse des données 3D du monde réel. Le traitement des données 3D est utile pour de nombreuses applications telles que la conduite autonome, la gestion du territoire, la surveillance des installations industrielles, l'inventaire forestier et la mesure de biomasse. Cependant, l'annotation et l'analyse des données 3D peuvent être exigeantes. En particulier, il est souvent difficile de respecter des contraintes liées à l'utilisation des ressources de calcul ou à l'efficacité de l'annotation. La difficulté d'interpréter et
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Nayak, Gaurav Kumar. "Data-efficient Deep Learning Algorithms for Computer Vision Applications." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6094.

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The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of training samples and hence these models are often referred to as ‘data-hungry’. However, large scale datasets may not always be available in practice due to proprietary/privacy reasons or because of the high cost of generation, annotation, transmission and storage of data. Hence, efficient utilization of available data is of utmost importance, and this gives rise to a class of ML
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Afreen, Ahmad. "Data Efficient Domain Generalization." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6047.

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Deep neural networks has brought tremendous success in many areas of computer vision, such as image classification, retrieval, segmentation , etc. However, this success is mostly measured under two conditions namely (1) the underlying distribution of the test data is the same as the distribution of the data used for training the network and (2) The classes available for testing is the same as the one in training. These assumptions are very restrictive in nature and may not hold in real-life. Since new data categories are continuously being discovered, so it is important for the trained m
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Wong, Jun Hua. "Efficient Edge Intelligence in the Era of Big Data." Thesis, 2021. http://hdl.handle.net/1805/26385.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attentions. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. Targeting this obstacle, we propose to investigate the biodynamic patterns in the data and design a data-driven approach for intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a spa
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Schwarzer, Max. "Data-efficient reinforcement learning with self-predictive representations." Thesis, 2020. http://hdl.handle.net/1866/25105.

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L'efficacité des données reste un défi majeur dans l'apprentissage par renforcement profond. Bien que les techniques modernes soient capables d'atteindre des performances élevées dans des tâches extrêmement complexes, y compris les jeux de stratégie comme le StarCraft, les échecs, le shogi et le go, ainsi que dans des domaines visuels exigeants comme les jeux Atari, cela nécessite généralement d'énormes quantités de données interactives, limitant ainsi l'application pratique de l'apprentissage par renforcement. Dans ce mémoire, nous proposons la SPR, une méthode inspirée des récentes avancée
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(11013474), Jun Hua Wong. "Efficient Edge Intelligence In the Era of Big Data." Thesis, 2021.

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Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attentions. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. Targeting this obstacle, we propose to investigate the biodynamic patterns in the data and design a data-driven approach for intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energ
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Ehrler, Matthew. "VConstruct: a computationally efficient method for reconstructing satellite derived Chlorophyll-a data." Thesis, 2021. http://hdl.handle.net/1828/13346.

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The annual phytoplankton bloom is an important marine event. Its annual variability can be easily recognized by ocean-color satellite sensors through the increase in surface Chlorophyll-a concentration, a key indicator to quantitatively characterize all phytoplankton groups. However, a common problem is that the satellites used to gather the data are obstructed by clouds and other artifacts. This means that time series data from satellites can suffer from spatial data loss. There are a number of algorithms that are able to reconstruct the missing parts of these images to varying d
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Books on the topic "Data-efficient Deep Learning"

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Jena, Om Prakash, Alok Ranjan Tripathy, Brojo Kishore Mishra, and Ahmed A. Elngar, eds. Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150404011220301.

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Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data proces
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Delgado Martín, Jordi, Andrea Muñoz-Ibáñez, and Ismael Himar Falcón-Suárez. 6th International Workshop on Rock Physics: A Coruña, Spain 13 -17 June 2022: Book of Abstracts. 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.000005.

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[Abstract] The 6th International Workshop on Rock Physics (6IWRP) was held A Coruña, Spain, between 13th and 17th of June, 2022. This meeting follows the track of the five successful encounters held in Golden (USA, 2011), Southampton (UK, 2013), Perth (Australia, 2015), Trondheim (Norway, 2017) and Hong Kong (China, 2019). The aim of the workshop was to bring together experiences allowing to illustrate, discuss and exchange recent advances in the wide realm of rock physics, including theoretical developments, in situ and laboratory scale experiments as well as digital analysis. While rock phys
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Book chapters on the topic "Data-efficient Deep Learning"

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Sarkar, Tirthajyoti. "Modular and Productive Deep Learning Code." In Productive and Efficient Data Science with Python. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8121-5_5.

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Folino, Francesco, Gianluigi Folino, Massimo Guarascio, and Luigi Pontieri. "Explainable Process Deviance Discovery with Data-Efficient Deep Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81241-5_6.

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Vepakomma, Praneeth, and Ramesh Raskar. "Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning." In Federated Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96896-0_19.

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Sharma, Pranav, Marcus Rüb, Daniel Gaida, Heiko Lutz, and Axel Sikora. "Deep Learning in Resource and Data Constrained Edge Computing Systems." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_5.

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AbstractTo demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C
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Maulana, Muhammad Rizki, and Wee Sun Lee. "Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning." In Machine Learning and Knowledge Discovery in Databases. Research Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86486-6_8.

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Ghantasala, G. S. Pradeep, L. R. Sudha, T. Veni Priya, P. Deepan, and R. Raja Vignesh. "An Efficient Deep Learning Framework for Multimedia Big Data Analytics." In Multimedia Computing Systems and Virtual Reality. CRC Press, 2022. http://dx.doi.org/10.1201/9781003196686-5.

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Zheng, Yefeng, David Liu, Bogdan Georgescu, Hien Nguyen, and Dorin Comaniciu. "Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning." In Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42999-1_4.

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Köklü, Ata, Yusuf Güven, and Tufan Kumbasar. "Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70018-7_46.

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Symeonidis, C., P. Nousi, P. Tosidis, et al. "Efficient Realistic Data Generation Framework Leveraging Deep Learning-Based Human Digitization." In Proceedings of the International Neural Networks Society. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_23.

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Rajawat, Anand Singh, Kanishk Barhanpurkar, S. B. Goyal, Pradeep Bedi, Rabindra Nath Shaw, and Ankush Ghosh. "Efficient Deep Learning for Reforming Authentic Content Searching on Big Data." In Advanced Computing and Intelligent Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2164-2_26.

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Conference papers on the topic "Data-efficient Deep Learning"

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Zhu, Xiaoke, Qi Zhang, Wei Zhou, and Ling Liu. "Deep Learning Service for Efficient Data Distribution Aware Sorting." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825633.

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A, Rhenius, Anitha J, and Immanuel Alex Pandian S. "Efficient Human Activity Recognition through Multi-Sensor Data and Deep Learning Techniques." In 2025 International Conference on Advanced Computing Technologies (ICoACT). IEEE, 2025. https://doi.org/10.1109/icoact63339.2025.11005004.

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Sharma, Nirdesh, Harshul Malik, Ria Joshi, and Manabendra Saharia. "Automated Landslide Extent Estimation from Sentinel 2 Data Using Computationally Efficient Deep Learning." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640552.

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Mir, Foudil, Dalil Hadjout, Abderrazak Sebaa, Abdelkader Laouid, and Farid Meziane. "A Data Communication Based on Deep Learning Model for Efficient IoT Energy Clustering." In 2024 1st International Conference on Innovative and Intelligent Information Technologies (IC3IT). IEEE, 2024. https://doi.org/10.1109/ic3it63743.2024.10869401.

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Othman, Nawaf Qasem Hamood, Haithm M. Al-Gunid, and Imran Younas. "Efficient Multi-UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10670944.

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Muller, Priya Shirley, K. Madhavi, Naga Durga Saile K, B. Prasanthi, B. Jayaram, and Goski Sathish. "A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10968940.

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Kafetzis, Ioannis, Alexander Hann, and George F. Fragulis. "Efficient Selection of Rare Pathology Samples from Unlabeled Medical Data via Deep Active Learning." In 2025 14th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2025. https://doi.org/10.1109/mocast65744.2025.11083944.

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R, Pushpa B., Harsh Shivhare, Aditya Raj, Anand Sambhaw, and Aman Verma. "Design And Development of Deep Learning Model of Chaotic Signal Processing for Efficient Data Encryption." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921821.

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Eddin, Maymouna Ez, Mohamed Massaoudi, Haitham Abu-Rub, et al. "Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks." In 2025 IEEE Texas Power and Energy Conference (TPEC). IEEE, 2025. https://doi.org/10.1109/tpec63981.2025.10906699.

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E, Syed Mohamed, Soundarapandiyan K, Dinesh Kumar S, and Syed Abdul Syed S. "An Efficient Blockchain and Deep Learning-Based Framework for Secure Healthcare Data Management and Disease Prediction." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020281.

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Reports on the topic "Data-efficient Deep Learning"

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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened att
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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