Relevant bibliographies by topics / Deep Convontional Neural Network

Contents

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

Academic literature on the topic 'Deep Convontional Neural Network'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Deep Convontional Neural Network"

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Jensen, Mitchell, Khamael Al-Dulaimi, Khairiyah Saeed Abduljabbar, and Jasmine Banks. "Automated Classification of Cell Level of HEp-2 Microscopic Images Using Deep Convolutional Neural Networks-Based Diameter Distance Features." JUCS - Journal of Universal Computer Science 29, no. (5) (2023): 432–45. https://doi.org/10.3897/jucs.96293.

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To identify autoimmune diseases in humans, analysis of HEp-2 staining patterns at cell level is the gold standard for clinical practice research communities. An automated procedure is a complicated task due to variations in cell densities, sizes, shapes and patterns, overfitting of features, large-scale data volume, stained cells and poor quality of images. Several machine learning methods that analyse and classify HEp-2 cell microscope images currently exist. However, accuracy is still not at the level required for medical applications and computer aided diagnosis due to those challenges. The
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Rajadnya, Prof Kirti. "Speech Recognition using Deep Neural Network Neural (DNN) and Deep Belief Network (DBN)." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 1543–48. http://dx.doi.org/10.22214/ijraset.2020.5359.

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Eom, Junsik, Sewon Kim, Hanbyol Jang, et al. "Neural spike classification via deep neural network." IBRO Reports 6 (September 2019): S139—S140. http://dx.doi.org/10.1016/j.ibror.2019.07.443.

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Szeto, Pok Man, Hamid Parvin, Mohammad Reza Mahmoudi, Bui Anh Tuan, and Kim-Hung Pho. "Deep neural network as deep feature learner." Journal of Intelligent & Fuzzy Systems 39, no. 1 (2020): 355–69. http://dx.doi.org/10.3233/jifs-191292.

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Ghasemi, Fahimeh, Alireza Mehridehnavi, Afshin Fassihi, and Horacio Pérez-Sánchez. "Deep neural network in QSAR studies using deep belief network." Applied Soft Computing 62 (January 2018): 251–58. http://dx.doi.org/10.1016/j.asoc.2017.09.040.

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Elbrachter, Dennis, Dmytro Perekrestenko, Philipp Grohs, and Helmut Bolcskei. "Deep Neural Network Approximation Theory." IEEE Transactions on Information Theory 67, no. 5 (2021): 2581–623. http://dx.doi.org/10.1109/tit.2021.3062161.

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Mashhadi, Peyman Sheikholharam, Sławomir Nowaczyk, and Sepideh Pashami. "Parallel orthogonal deep neural network." Neural Networks 140 (August 2021): 167–83. http://dx.doi.org/10.1016/j.neunet.2021.03.002.

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FUKUSHIMA, Kunihiko. "Neocognitron: Deep Convolutional Neural Network." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 4 (2015): 115–25. http://dx.doi.org/10.3156/jsoft.27.4_115.

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Choi, Yoojin, Mostafa El-Khamy, and Jungwon Lee. "Universal Deep Neural Network Compression." IEEE Journal of Selected Topics in Signal Processing 14, no. 4 (2020): 715–26. http://dx.doi.org/10.1109/jstsp.2020.2975903.

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Majumdar, Somshubra, and Ishaan Jain. "Deep Columnar Convolutional Neural Network." International Journal of Computer Applications 145, no. 12 (2016): 25–32. http://dx.doi.org/10.5120/ijca2016910772.

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Dissertations / Theses on the topic "Deep Convontional Neural Network"

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Kapoor, Rishika. "Malaria Detection Using Deep Convolution Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749143868579.

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Pajot, Arthur. "Incorporating physical knowledge into deep neural network." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS290.

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Un processus physique est un phénomène marqué par des changements graduels à travers une série d'états successifs se produisant dans le monde physique. Les physiciens et les climatologues tentent de modéliser ces processus d'une manière fondée sur le principe de descriptions analytiques des connaissances a priori des processus sous-jacents. Malgré le succès indéniable de l'apprentissage profond, une approche entièrement axée sur les données n'est pas non plus encore prête à remettre en question l'approche classique de modélisation des systèmes dynamiques. Nous tenterons de démontrer dans cette
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Heuillet, Alexandre. "Exploring deep neural network differentiable architecture design." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG069.

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L'intelligence artificielle (IA) a gagné en popularité ces dernières années, principalement en raison de ses applications réussies dans divers domaines tels que l'analyse de données textuelles, la vision par ordinateur et le traitement audio. La résurgence des techniques d'apprentissage profond a joué un rôle central dans ce succès. L'article révolutionnaire de Krizhevsky et al., AlexNet, a réduit l'écart entre les performances humaines et celles des machines dans les tâches de classification d'images. Des articles ultérieurs tels que Xception et ResNet ont encore renforcé l'apprentissage prof
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Verma, Sagar. "Deep Neural Network Modeling of Electric Motors." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST088.

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Cette thèse traite de l’application des réseaux de neurones dans la résolution de problèmes liés aux moteurs électriques. Le chapitre 2 contribue à identifier une structure de réseau de neurones capable d’apprendre la relation multi-variée entre différents signaux d’un moteur électrique. La structure identifiée est ensuite utilisée pour l’estimation vitesse- couple à partir des courants et des tensions.Le chapitre 3 se concentre sur la détection et la correction de défauts de mesure. Notre méthode prend en compte les défauts de capteurs électriques, les défauts mécaniques et l’estimation de tempé
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Dupont, Robin. "Deep Neural Network Compression for Visual Recognition." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS565.

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Grâce à la miniaturisation de l'électronique, les dispositifs embarqués sont devenus omniprésents depuis les années 2010, réalisant diverses tâches autour de nous. À mesure que leur utilisation augmente, la demande pour des dispositifs traitant les données et prenant des décisions complexes de manière efficace s'intensifie. Les réseaux de neurones profonds sont puissants pour cet objectif, mais souvent trop lourds pour les appareils embarqués. Il est donc impératif de compresser ces réseaux sans compromettre leur performance. Cette thèse introduit deux méthodes innovantes centrées sur l'élagag
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Aspandi, Latif Decky. "Deep spatio-temporal neural network for facial analysis." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/671209.

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Automatic Facial Analysis is one of the most important field of computer vision due to its significant impacts to the world we currently live in. Among many applications of Automatic Facial Analysis, Facial Alignment and Facial-Based Emotion Recognition are two most prominent tasks considering their roles in this field. That is, the former serves as intermediary steps enabling many higher facial analysis tasks, and the latter provides direct, real-world high level facial-based analysis and applications to the society. Together, they have significant impacts ranging from biometric recognition,
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Alpire, Adam. "Predicting Solar Radiation using a Deep Neural Network." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215715.

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Simulating the global climate in fine granularity is essential in climate science research. Current algorithms for computing climate models are based on mathematical models that are computationally expensive. Climate simulation runs can take days or months to execute on High Performance Computing (HPC) platforms. As such, the amount of computational resources determines the level of resolution for the simulations. If simulation time could be reduced without compromising model fidelity, higher resolution simulations would be possible leading to potentially new insights in climate science resear
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Redkar, Shrutika. "Deep Learning Binary Neural Network on an FPGA." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/407.

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In recent years, deep neural networks have attracted lots of attentions in the field of computer vision and artificial intelligence. Convolutional neural network exploits spatial correlations in an input image by performing convolution operations in local receptive fields. When compared with fully connected neural networks, convolutional neural networks have fewer weights and are faster to train. Many research works have been conducted to further reduce computational complexity and memory requirements of convolutional neural networks, to make it applicable to low-power embedded applications. T
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Shuvo, Md Kamruzzaman. "Hardware Efficient Deep Neural Network Implementation on FPGA." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2792.

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In recent years, there has been a significant push to implement Deep Neural Networks (DNNs) on edge devices, which requires power and hardware efficient circuits to carry out the intensive matrix-vector multiplication (MVM) operations. This work presents hardware efficient MVM implementation techniques using bit-serial arithmetic and a novel MSB first computation circuit. The proposed designs take advantage of the pre-trained network weight parameters, which are already known in the design stage. Thus, the partial computation results can be pre-computed and stored into look-up tables. Then the
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Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

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Books on the topic "Deep Convontional Neural Network"

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Gridin, Ivan. Automated Deep Learning Using Neural Network Intelligence. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8149-9.

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Toriumi, Mitsuhiro. Geochemical Mechanics and Deep Neural Network Modeling. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3659-3.

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Alsuhli, Ghada, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, and Thanos Stouraitis. Number Systems for Deep Neural Network Architectures. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-38133-1.

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Shanthini, A., Gunasekaran Manogaran, and G. Vadivu. Deep Convolutional Neural Network for The Prognosis of Diabetic Retinopathy. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3877-1.

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Toriumi, Mitsuhiro. Physics of Geochemical Mechanics and Deep Neural Network Modeling with Diffusion Augmentation. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9376-1.

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A, Renzetti N., and Jet Propulsion Laboratory (U.S.), eds. The Deep Space Network as an instrument for radio science research: Power system stability applications of artificial neural networks. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1993.

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Osipyan, Hasmik, Bosede Edwards, and Adrian David Cheok. Deep Neural Network Applications. CRC Press LLC, 2022.

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Cheok, Adrian David. Deep Neural Network Applications. Taylor & Francis Group, 2022.

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Cheok, Adrian David. Deep Neural Network Applications. Taylor & Francis Group, 2022.

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Cheok, Adrian David. Deep Neural Network Applications. Taylor & Francis Group, 2022.

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Book chapters on the topic "Deep Convontional Neural Network"

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Kim, Phil. "Neural Network." In MATLAB Deep Learning. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2845-6_2.

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Farahani, Hojjatollah, Marija Blagojević, Parviz Azadfallah, Peter Watson, Forough Esrafilian, and Sara Saljoughi. "Deep Neural Network." In An Introduction to Artificial Psychology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31172-7_6.

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El-Amir, Hisham, and Mahmoud Hamdy. "Convolutional Neural Network." In Deep Learning Pipeline. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5349-6_11.

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Kim, Phil. "Convolutional Neural Network." In MATLAB Deep Learning. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2845-6_6.

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Osipyan, Hasmik, Bosede Iyiade Edwards, and Adrian David Cheok. "Neural Network Structures." In Deep Neural Network Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9780429265686-3.

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Bhasin, Harsh. "Recurrent Neural Network." In Hands-on Deep Learning. Apress, 2024. https://doi.org/10.1007/979-8-8688-1035-0_9.

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Lee, Taesam, Vijay P. Singh, and Kyung Hwa Cho. "Neural Network." In Deep Learning for Hydrometeorology and Environmental Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64777-3_4.

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Kim, Phil. "Neural Network and Classification." In MATLAB Deep Learning. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2845-6_4.

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Tan, DongXu, JunMin Wu, HuanXin Zheng, Yan Yin, and YaXin Liu. "Fissionable Deep Neural Network." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46681-1_44.

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Long, Liangqu, and Xiangming Zeng. "Recurrent Neural Network." In Beginning Deep Learning with TensorFlow. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7915-1_11.

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Conference papers on the topic "Deep Convontional Neural Network"

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Song, Jiafei, Yun Zhao, Zhijia Wang, and Yan Zhang. "A new feed-forward deep neural network: mobile dense neural network." In Second International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2025), edited by Fabio Tosti and Román Alvarez. SPIE, 2025. https://doi.org/10.1117/12.3067696.

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Rastgoftar, Hossein, and Muhammad J. H. Zahed. "Deep Neural Network-Based UAS Transport." In 2025 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2025. https://doi.org/10.1109/icuas65942.2025.11007873.

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Zhong, Nan, Zhenxing Qian, and Xinpeng Zhang. "Deep Neural Network Retrieval." In MM '21: ACM Multimedia Conference. ACM, 2021. http://dx.doi.org/10.1145/3474085.3475505.

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Carvalho, Marcus, and Mahardhika Pratama. "Improving shallow neural network by compressing deep neural network." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628686.

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Arora, Divya, Mehak Garg, and Megha Gupta. "Diving deep in Deep Convolutional Neural Network." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2020. http://dx.doi.org/10.1109/icacccn51052.2020.9362907.

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Ghahremani, Pegah, Jasha Droppo, and Michael L. Seltzer. "Linearly augmented deep neural network." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472646.

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Ghahremani, Pegah, and Jasha Droppo. "Self-stabilized deep neural network." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472719.

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Park, Jae Hyun, Ji Sub Choi, and Jong Hwan Ko. "Dual-Precision Deep Neural Network." In AIPR 2020: 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition. ACM, 2020. http://dx.doi.org/10.1145/3430199.3430228.

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Hegazy, Ali, and Khaled Salah. "Deep Neural Network Inference Processor." In 2023 International Conference on Microelectronics (ICM). IEEE, 2023. http://dx.doi.org/10.1109/icm60448.2023.10378906.

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Mahmoud, Loreen, and Raja Praveen. "Network Security Evaluation Using Deep Neural Network." In 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2020. http://dx.doi.org/10.23919/icitst51030.2020.9351326.

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Reports on the topic "Deep Convontional Neural Network"

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Ma, Yangyue. Sphere decoding based on Deep Neural Network. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-1320.

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Chronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell, and Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202315.

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In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments,
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Mu, Ruihui. A Novel Recommendation Model Based on Deep Neural Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.05.11.

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Reddy, S., and A. Crisp. Deep Neural Network Informed Markov Chain Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2283285.

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D'Elia, Marta, Stewart Silling, Yue Yu, Huaiqian You, and Tian Gao. Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1855045.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.cswp.04222019.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.cswp.8.

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Cordaro, Samuel. Z-Target Radiography Postprocessing With A Deep Convolution Neural Network. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480145.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl
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Rocco, Dominick Rosario. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1294514.

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