Relevant bibliographies by topics / Convolution Neural Networks

Contents

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

Academic literature on the topic 'Convolution Neural Networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Convolution Neural Networks"

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Liao, Shengbin, Xiaofeng Wang, and ZongKai Yang. "A heterogeneous two-stream network for human action recognition." AI Communications 36, no. 3 (2023): 219–33. http://dx.doi.org/10.3233/aic-220188.

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The most widely used two-stream architectures and building blocks for human action recognition in videos generally consist of 2D or 3D convolution neural networks. 3D convolution can abstract motion messages between video frames, which is essential for video classification. 3D convolution neural networks usually obtain good performance compared with 2D cases, however it also increases computational cost. In this paper, we propose a heterogeneous two-stream architecture which incorporates two convolutional networks. One uses a mixed convolution network (MCN), which combines some 3D convolutions
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Sapunov, V. V., S. A. Botman, G. V. Kamyshov, and N. N. Shusharina. "Application of Convolution with Periodic Boundary Condition for Processing Data from Cylindrical Electrode Arrays." INFORMACIONNYE TEHNOLOGII 27, no. 3 (2021): 125–31. http://dx.doi.org/10.17587/it.27.125-131.

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In this paper, modification of convolutional neural networks for purposes of processing electromyographic data obtained from cylindrical arrays of electrodes was proposed. Taking into account the spatial symmetry of the array, convolution operation was redefined using periodic boundary conditions, which allowed to construct a neural network that is invariant to rotations of electrodes array around its axis. Applicability of the proposed approach was evaluated by constructing a neural network containing a new type of convolutional layer and training it on the open UC2018 DualMyo dataset in orde
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Yang, Dengjie, Zhiwei Chen, Yi Sun, Qing He, Shiwei Ye, and Deyuan Chen. "EKConv: Compressing Convolutional Neural Networks with Evolutionary Kernel Convolution." Journal of Physics: Conference Series 2425, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2425/1/012011.

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Abstract Convolutional neural networks (CNNs) have achieved tremendous success in visual recognition tasks but mainly rely on massive learnable parameters. To solve this problem, many effective and efficient convolution operators have been proposed, such as group-wise convolution, point-wise convolution, and depth-wise convolution. However, the above convolution operations model and optimize the weight relationship within the same convolutional layer. To reduce the network parameters, we explicitly construct the relationship between convolution kernels of adjacent layers. Specifically, we prop
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Zhang, Yulin, Feipeng Li, Haoke Xu, Xiaoming Li, and Shan Jiang. "Efficient Convolutional Neural Networks Utilizing Fine-Grained Fast Fourier Transforms." Electronics 13, no. 18 (2024): 3765. http://dx.doi.org/10.3390/electronics13183765.

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Convolutional Neural Networks (CNNs) are among the most prevalent deep learning techniques employed across various domains. The computational complexity of CNNs is largely attributed to the convolution operations. These operations are computationally demanding and significantly impact overall model performance. Traditional CNN implementations convert convolutions into matrix operations via the im2col (image to column) technique, facilitating parallelization through advanced BLAS libraries. This study identifies and investigates a significant yet intricate pattern of data redundancy within the
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Geum, Young Hee, Arjun Kumar Rathie, and Hwajoon Kim. "Matrix Expression of Convolution and Its Generalized Continuous Form." Symmetry 12, no. 11 (2020): 1791. http://dx.doi.org/10.3390/sym12111791.

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In this paper, we consider the matrix expression of convolution, and its generalized continuous form. The matrix expression of convolution is effectively applied in convolutional neural networks, and in this study, we correlate the concept of convolution in mathematics to that in convolutional neural network. Of course, convolution is a main process of deep learning, the learning method of deep neural networks, as a core technology. In addition to this, the generalized continuous form of convolution has been expressed as a new variant of Laplace-type transform that, encompasses almost all exis
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Feng, Yifan, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. "Hypergraph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3558–65. http://dx.doi.org/10.1609/aaai.v33i01.33013558.

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In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be
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D P, Mrs Manju Bhargavi, Charmala Rajesh Karna, harsha A S, and harsha H. "Estimating Indian Food Calorie With Convolution Neural Networks." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12846–50. https://doi.org/10.55248/gengpi.6.0525.1908.

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Liu, Zhizhe, Luo Sun, and Qian Zhang. "High Similarity Image Recognition and Classification Algorithm Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2836486.

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Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train
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Syamala Rao, P., Dr G.P.SaradhiVarma, and Rajasekhar Mutukuri. "Effective and High Computing Algorithms for Convolution Neural Networks." International Journal of Engineering & Technology 7, no. 3.31 (2018): 66. http://dx.doi.org/10.14419/ijet.v7i3.31.18203.

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Training a large set of data takes GPU days using Deep convolution neural networks which are a time taking process. Self-driving cars require very low latency for pedestrian detection. Image recognition constrained by limited processing resources for mobile phones. The computation speed of the training set determines that in these situations convolution neural networks was a success. For large filters, Conventional Faster Fourier Transform based convolution is preferably fast, yet in case of small, 3 × 3 filters state of the art convolutional neural networks is used. By using Winograd's minima
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Xianhao Shen, Xianhao Shen, Changhong Zhu Xianhao Shen, Yihao Zang Changhong Zhu, and Shaohua Niu Yihao Zang. "A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network." 電腦學刊 33, no. 3 (2022): 049–58. http://dx.doi.org/10.53106/199115992022063303004.

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<p>Abnormal data detection is an important step to ensure the accuracy and reliability of node data in wireless sensor networks. In this paper, a data classification method based on convolutional neural network is proposed to solve the problem of data anomaly detection in wireless sensor networks. First, Normal data and abnormal data generated after injection fault are normalized and mapped to gray image as input data of the convolutional neural network. Then, based on the classical convolution neural network, three new convolutional neural network models are designed by designing the pa
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Dissertations / Theses on the topic "Convolution Neural Networks"

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Messou, Ehounoud Joseph Christopher. "Handling Invalid Pixels in Convolutional Neural Networks." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98619.

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Most neural networks use a normal convolutional layer that assumes that all input pixels are valid pixels. However, pixels added to the input through padding result in adding extra information that was not initially present. This extra information can be considered invalid. Invalid pixels can also be inside the image where they are referred to as holes in completion tasks like image inpainting. In this work, we look for a method that can handle both types of invalid pixels. We compare on the same test bench two methods previously used to handle invalid pixels outside the image (Partial and Edg
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Ngo, Kalle. "FPGA Hardware Acceleration of Inception Style Parameter Reduced Convolution Neural Networks." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205026.

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Some researchers have noted that the growth rate in the number of network parameters of many recently proposed state-of-the-art CNN topologies is placing unrealistic demands on hardware resources and limits the practical applications of Neural Networks. This is particularly apparent when considering many of the projected applications (IoT, autonomous vehicles, etc) utilize embedded systems with even greater restrictions on computation and memory bandwidth than the typical research-class computer cluster that the CNN was designed on. The GoogLeNet CNN in 2014 proposed a new level of organizatio
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Ioannou, Yani Andrew. "Structural priors in deep neural networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278976.

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Deep learning has in recent years come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite breakthroughs in training deep networks, there remains a lack of understanding of both the optimization and structure of deep networks. The approach advocated by many researchers in the field has been to train monolithic networks with excess complexity, and strong regularization --- an approach that leaves much to desire in efficiency. Instead we propose that carefully designing networks in considerati
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Pappone, Francesco. "Graph neural networks: theory and applications." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23893/.

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Le reti neurali artificiali hanno visto, negli ultimi anni, una crescita vertiginosa nelle loro applicazioni e nelle architetture dei modelli impiegati. In questa tesi introduciamo le reti neurali su domini euclidei, in particolare mostrando l’importanza dell’equivarianza di traslazione nelle reti convoluzionali, e introduciamo, per analogia, un’estensione della convoluzione a dati strutturati come grafi. Inoltre presentiamo le architetture dei principali Graph Neural Network ed esponiamo, per ognuna delle tre architetture proposte (Spectral graph Convolutional Network, Graph Co
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Jackman, Simeon. "Football Shot Detection using Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157438.

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In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architecture
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Sung, Wei-Hong. "Investigating minimal Convolution Neural Networks (CNNs) for realtime embedded eye feature detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281338.

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With the rapid rise of neural networks, many tasks that used to be difficult to complete in traditional methods can now be solved well, especially in the computer vision field. However, as the tasks we have to solve have become more and more complex, the neural networks we use are becoming deeper and larger. Therefore, although some embedded systems are powerful nowadays, most embedded systems still suffer from memory and computation limitations, which means it is hard to deploy our large neural networks on these embedded devices. This project aims to explore different methods to compress the
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Wu, Jindong. "Pooling strategies for graph convolution neural networks and their effect on classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288953.

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With the development of graph neural networks, this novel neural network has been applied in a broader and broader range of fields. One of the thorny problems researchers face in this field is selecting suitable pooling methods for a specific research task from various existing pooling methods. In this work, based on the existing mainstream graph pooling methods, we develop a benchmark neural network framework that can be used to compare these different graph pooling methods. By using the framework, we compare four mainstream graph pooling methods and explore their characteristics. Furthermore
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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the
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Elavarthi, Pradyumna. "Semantic Segmentation of RGB images for feature extraction in Real Time." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573575765136448.

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Highlander, Tyler. "Efficient Training of Small Kernel Convolutional Neural Networks using Fast Fourier Transform." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1432747175.

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Books on the topic "Convolution Neural Networks"

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Mou, Lili, and Zhi Jin. Tree-Based Convolutional Neural Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1870-2.

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Milosevic, Nemanja. Introduction to Convolutional Neural Networks. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5648-0.

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Habibi Aghdam, Hamed, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57550-6.

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Venkatesan, Ragav, and Baoxin Li. Convolutional Neural Networks in Visual Computing. CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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Teoh, Teik Toe. Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8814-1.

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Koonce, Brett. Convolutional Neural Networks with Swift for Tensorflow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2.

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Ozturk, Saban. Convolutional Neural Networks for Medical Image Processing Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003215141.

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Naved, Mohd, V. Ajantha Devi, Loveleen Gaur, and Ahmed A. Elngar. IoT-enabled Convolutional Neural Networks: Techniques and Applications. River Publishers, 2023. http://dx.doi.org/10.1201/9781003393030.

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Ally, Afshan. A Hopfield neural network decoder for convolutional codes. National Library of Canada = Bibliothèque nationale du Canada, 1991.

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Khan, Salman, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun. A Guide to Convolutional Neural Networks for Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01821-3.

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Book chapters on the topic "Convolution Neural Networks"

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Dai, Qionghai, and Yue Gao. "Neural Networks on Hypergraph." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_7.

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AbstractWith the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods. For the spectral-based methods, the convolution operation is formulated in the spectral domain of graph, and we introduce the typical spectral-based methods, including hypergraph neural networks (HGNN), hypergraph convolution with attention (Hyper-Atten), and hyperbolic hypergraph neural network (HHGNN)
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Bourahla, Mustapha. "Quantum Convolution for Convolutional Neural Networks." In Quantum Computing: Applications and Challenges. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59318-5_14.

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Bhuyan, Bikram Pratim, Amar Ramdane-Cherif, Thipendra P. Singh, and Ravi Tomar. "Convolution in Neural Networks." In Studies in Computational Intelligence. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-8171-3_8.

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Yang, Dengjie, Xuehui Yu, Yi Sun, Fuzhen Zhuang, Qing He, and Shiwei Ye. "BFConv: Improving Convolutional Neural Networks with Butterfly Convolution." In Neural Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92273-3_4.

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Guo, Yanming, Songyang Lao, Yu Liu, Liang Bai, Shi Liu, and Michael S. Lew. "Convolutional Neural Networks Features: Principal Pyramidal Convolution." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24075-6_24.

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Pattabiraman, V., and R. Maheswari. "Image to Text Processing Using Convolution Neural Networks." In Recurrent Neural Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822-4.

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Curra-Sosa, Dagnier A., Ricardo Tapiador-Morales, Francisco Gómez-Rodríguez, and Alejandro Linares-Barranco. "Spiking Convolution Engine for Spiking Convolution Neural Networks." In Springer Proceedings in Materials. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64106-0_43.

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Tang, Lihe, Weidong Yang, Qiang Gao, Rui Xu, and Rongzhi Ye. "A Lightweight Verification Scheme Based on Dynamic Convolution." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_78.

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AbstractSince Electricity Grid Engineering involves a large number of personnel in the construction process, face recognition algorithms can be used to solve the personnel management problem. The recognition devices used in Electricity Grid Engineering are often mobile, embedded, and other lightweight devices with limited hardware performance. Although a large number of existing face recognition algorithms based on deep convolutional neural networks have high recognition accuracy, they are difficult to run in mobile devices or offline environments due to high computational complexity. In order
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Su, Zhuo, Linpu Fang, Wenxiong Kang, Dewen Hu, Matti Pietikäinen, and Li Liu. "Dynamic Group Convolution for Accelerating Convolutional Neural Networks." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58539-6_9.

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Saidulu, Golla, B. Gayathri, Najeema Afreen, et al. "Plant Recognition Using Convolution Neural Networks." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81083-1_35.

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Conference papers on the topic "Convolution Neural Networks"

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Tiwari, Aarushi, Julian Gamboa, Tabassom Hamidfar, Xi Shen, Shamima A. Mitu, and Selim M. Shahriar. "Opto-electronic Balanced Joint Transform Correlator as a Convolution Stage for Neural Networks." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jtu4a.55.

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Opto-electronic correlators are able to perform high-speed convolutions with minimal processing and power requirements. Here, we propose the use of a balanced joint transform correlator as a convolution stage for a convolutional neural network.
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Santhoshi, M., K. Mohanthi, P. Venugopal, and H. L. Shilpashree. "Car Model Recognition Using Convolution Neural Networks." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10775205.

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Adeyemo, Ayodeji, Albert Carlson, and Michael Totaro. "Sexing Orcas: Classification Using Convolution Neural Networks." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00286.

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Yuchang, Zhu, and Xiao Nanfeng. "Lane Detection Method Based on Deformable Linear Convolution." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650782.

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Almulla, Mohammed A. "Facial Expression Recognition Using Deep Convolution Neural Networks." In 2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiot63253.2024.00022.

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Wang, Jingyao, Issam Falih, and Emmanuel Bergeret. "Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651306.

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Zhu, Ying, Kailai Liu, Yuhan Gong, et al. "An Optoelectronic Neuromorphic Accelerator with over 1 TOPS for Real-Valued and over 3 TOPS for Complex-Valued Vectors." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th2a.35.

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A scalable and integrable optoelectronic neuromorphic accelerator is demonstrated, achieving computing speeds of 1.024TOPS and 3.072TOPS for real-valued and complex-valued convolutions, respectively. It yields competitive accuracies in processing both real-valued and complex-valued convolution neural networks.
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Ramanan, B., S. Bhuvaneswari, Prashanth C. Upadhya, R. Venkateswaran, and K. V. Sriram. "Two-stage deep learning pipeline for automated mineral classification in planetary Raman spectra." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jtu2a.208.

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A novel approach is employed to improve mineral classification in planetary Raman spectra using a two-stage deep learning pipeline utilizing convolutional denoising autoencoders for pre-processing and convolution neural networks to classify minerals.
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Paval, Ks, Vishnu Radhakrishnan, Km Krishnan, G. Jyothish Lal, and B. Premjith. "Multimodal Fusion for Abusive Speech Detection Using Liquid Neural Networks and Convolution Neural Network." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724438.

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Vieira, Caio, Arthur Lorenzon, Lucas Schnorr, Philippe Navaux, and Antonio Carlos Beck. "Exploring Direct Convolution Performance on the Gemmini Accelerator." In XXI Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/wscad.2020.14067.

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Convolutional Neural Network (CNN) algorithms are becoming a recurrent solution to solve Computer Vision related problems. These networks employ convolutions as main building block, which greatly impact their performance since convolution is a costly operation. Due to its importance in CNN algorithms, this work evaluates convolution performance in the Gemmini accelerator and compare it to a conventional lightlyand heavily-loaded desktop CPU in terms of execution time and energy consumption. We show that Gemmini can achieve lower execution time and energy consumption when compared to a CPU even
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Reports on the topic "Convolution Neural Networks"

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Saha, Roshni. Classification of Parkinson’s Disease Using MRI Data and Deep Learning Convolution Neural Networks. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-362.

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio
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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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Forrest, Robert. Convolutional Neural Networks for Signal Detection. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1813655.

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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can
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Guan, Hui, Xipeng Shen, Seung-Hwan Lim, and Robert M. Patton. Composability-Centered Convolutional Neural Network Pruning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1427608.

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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Tilton, Miranda. CoNNOR: Convolutional Neural Network for Outsole Recognition. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-416.

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Shao, Lu. Automatic Seizure Detection based on a Convolutional Neural Network-Recurrent Neural Network Model. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-269.

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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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