Journal articles on the topic 'ReLU neural networks'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'ReLU neural networks.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
Liang, XingLong, and Jun Xu. "Biased ReLU neural networks." Neurocomputing 423 (January 2021): 71–79. http://dx.doi.org/10.1016/j.neucom.2020.09.050.
Full textHuang, Changcun. "ReLU Networks Are Universal Approximators via Piecewise Linear or Constant Functions." Neural Computation 32, no. 11 (November 2020): 2249–78. http://dx.doi.org/10.1162/neco_a_01316.
Full textKulathunga, Nalinda, Nishath Rajiv Ranasinghe, Daniel Vrinceanu, Zackary Kinsman, Lei Huang, and Yunjiao Wang. "Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks." Algorithms 14, no. 2 (February 5, 2021): 51. http://dx.doi.org/10.3390/a14020051.
Full textDung, D., V. K. Nguyen, and M. X. Thao. "ON COMPUTATION COMPLEXITY OF HIGH-DIMENSIONAL APPROXIMATION BY DEEP ReLU NEURAL NETWORKS." BULLETIN of L.N. Gumilyov Eurasian National University. MATHEMATICS. COMPUTER SCIENCE. MECHANICS Series 133, no. 4 (2020): 8–18. http://dx.doi.org/10.32523/2616-7182/2020-133-4-8-18.
Full textGühring, Ingo, Gitta Kutyniok, and Philipp Petersen. "Error bounds for approximations with deep ReLU neural networks in Ws,p norms." Analysis and Applications 18, no. 05 (September 19, 2019): 803–59. http://dx.doi.org/10.1142/s0219530519410021.
Full textDũng, Dinh, Van Kien Nguyen, and Mai Xuan Thao. "COMPUTATION COMPLEXITY OF DEEP RELU NEURAL NETWORKS IN HIGH-DIMENSIONAL APPROXIMATION." Journal of Computer Science and Cybernetics 37, no. 3 (September 28, 2021): 291–320. http://dx.doi.org/10.15625/1813-9663/37/3/15902.
Full textПолковникова, Н. А., Е. В. Тузинкевич, and А. Н. Попов. "Application of convolutional neural networks for monitoring of marine objects." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 4(50) (December 17, 2020): 53–61. http://dx.doi.org/10.37220/mit.2020.50.4.097.
Full textGao, Hongyang, Lei Cai, and Shuiwang Ji. "Adaptive Convolutional ReLUs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3914–21. http://dx.doi.org/10.1609/aaai.v34i04.5805.
Full textPetzka, Henning, Martin Trimmel, and Cristian Sminchisescu. "Notes on the Symmetries of 2-Layer ReLU-Networks." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5150.
Full textZheng, Shuxin, Qi Meng, Huishuai Zhang, Wei Chen, Nenghai Yu, and Tie-Yan Liu. "Capacity Control of ReLU Neural Networks by Basis-Path Norm." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5925–32. http://dx.doi.org/10.1609/aaai.v33i01.33015925.
Full textLiu, Bo, and Yi Liang. "Optimal function approximation with ReLU neural networks." Neurocomputing 435 (May 2021): 216–27. http://dx.doi.org/10.1016/j.neucom.2021.01.007.
Full textBodyanskiy, Yevgeniy, and Serhii Kostiuk. "Adaptive hybrid activation function for deep neural networks." System research and information technologies, no. 1 (April 25, 2022): 87–96. http://dx.doi.org/10.20535/srit.2308-8893.2022.1.07.
Full textMoon, Sunghwan. "ReLU Network with Bounded Width Is a Universal Approximator in View of an Approximate Identity." Applied Sciences 11, no. 1 (January 4, 2021): 427. http://dx.doi.org/10.3390/app11010427.
Full textBai, Yuhan. "RELU-Function and Derived Function Review." SHS Web of Conferences 144 (2022): 02006. http://dx.doi.org/10.1051/shsconf/202214402006.
Full textAbdeljawad, Ahmed, and Philipp Grohs. "Approximations with deep neural networks in Sobolev time-space." Analysis and Applications 20, no. 03 (May 2022): 499–541. http://dx.doi.org/10.1142/s0219530522500014.
Full textsci, Juncai He. "Relu Deep Neural Networks and Linear Finite Elements." Journal of Computational Mathematics 38, no. 3 (June 2020): 502–27. http://dx.doi.org/10.4208/jcm.1901-m2018-0160.
Full textDũng, Dinh, and Van Kien Nguyen. "Deep ReLU neural networks in high-dimensional approximation." Neural Networks 142 (October 2021): 619–35. http://dx.doi.org/10.1016/j.neunet.2021.07.027.
Full textDureja, Aman, and Payal Pahwa. "Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks." Recent Patents on Computer Science 12, no. 3 (May 8, 2019): 156–61. http://dx.doi.org/10.2174/2213275911666181025143029.
Full textLi, Qunwei, Shaofeng Zou, and Wenliang Zhong. "Learning Graph Neural Networks with Approximate Gradient Descent." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8438–46. http://dx.doi.org/10.1609/aaai.v35i10.17025.
Full textSun, Yichen, Mingli Dong, Mingxin Yu, Jiabin Xia, Xu Zhang, Yuchen Bai, Lidan Lu, and Lianqing Zhu. "Nonlinear All-Optical Diffractive Deep Neural Network with 10.6 μm Wavelength for Image Classification." International Journal of Optics 2021 (February 27, 2021): 1–16. http://dx.doi.org/10.1155/2021/6667495.
Full textThakur, Amey. "Fundamentals of Neural Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 407–26. http://dx.doi.org/10.22214/ijraset.2021.37362.
Full textBotoeva, Elena, Panagiotis Kouvaros, Jan Kronqvist, Alessio Lomuscio, and Ruth Misener. "Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3291–99. http://dx.doi.org/10.1609/aaai.v34i04.5729.
Full textPłaczek, Stanisław, and Aleksander Płaczek. "Learning algorithm analysis for deep neural network with ReLu activation functions." ITM Web of Conferences 19 (2018): 01009. http://dx.doi.org/10.1051/itmconf/20181901009.
Full textHe, Juncai, Lin Li, and Jinchao Xu. "ReLU deep neural networks from the hierarchical basis perspective." Computers & Mathematics with Applications 120 (August 2022): 105–14. http://dx.doi.org/10.1016/j.camwa.2022.06.006.
Full textDey, Santanu S., Guanyi Wang, and Yao Xie. "Approximation Algorithms for Training One-Node ReLU Neural Networks." IEEE Transactions on Signal Processing 68 (2020): 6696–706. http://dx.doi.org/10.1109/tsp.2020.3039360.
Full textLiu, Wan-Wei, Fu Song, Tang-Hao-Ran Zhang, and Ji Wang. "Verifying ReLU Neural Networks from a Model Checking Perspective." Journal of Computer Science and Technology 35, no. 6 (November 2020): 1365–81. http://dx.doi.org/10.1007/s11390-020-0546-7.
Full textChieng, Hock Hung, Noorhaniza Wahid, Ong Pauline, and Sai Raj Kishore Perla. "Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning." International Journal of Advances in Intelligent Informatics 4, no. 2 (July 31, 2018): 76. http://dx.doi.org/10.26555/ijain.v4i2.249.
Full textSalam, Abdulwahed, Abdelaaziz El Hibaoui, and Abdulgabbar Saif. "A comparison of activation functions in multilayer neural network for predicting the production and consumption of electricity power." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 163. http://dx.doi.org/10.11591/ijece.v11i1.pp163-170.
Full textYuan, Xiaoyong, Zheng Feng, Matthew Norton, and Xiaolin Li. "Generalized Batch Normalization: Towards Accelerating Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1682–89. http://dx.doi.org/10.1609/aaai.v33i01.33011682.
Full textButt, F. M., L. Hussain, S. H. M. Jafri, K. J. Lone, M. Alajmi, I. Abunadi, F. N. Al-Wesabi, and M. A. Hamza. "Optimizing Parameters of Artificial Intelligence Deep Convolutional Neural Networks (CNN) to improve Prediction Performance of Load Forecasting System." IOP Conference Series: Earth and Environmental Science 1026, no. 1 (May 1, 2022): 012028. http://dx.doi.org/10.1088/1755-1315/1026/1/012028.
Full textLee, Seunghye, Qui X. Lieu, Thuc P. Vo, and Jaehong Lee. "Deep Neural Networks for Form-Finding of Tensegrity Structures." Mathematics 10, no. 11 (May 25, 2022): 1822. http://dx.doi.org/10.3390/math10111822.
Full textAkintunde, Michael E., Andreea Kevorchian, Alessio Lomuscio, and Edoardo Pirovano. "Verification of RNN-Based Neural Agent-Environment Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6006–13. http://dx.doi.org/10.1609/aaai.v33i01.33016006.
Full textDaróczy, Bálint. "Gaussian Perturbations in ReLU Networks and the Arrangement of Activation Regions." Mathematics 10, no. 7 (March 31, 2022): 1123. http://dx.doi.org/10.3390/math10071123.
Full textZheng, Jing, Shuaishuai Shen, Tianqi Jiang, and Weiqiang Zhu. "Deep neural networks design and analysis for automatic phase pickers from three-component microseismic recordings." Geophysical Journal International 220, no. 1 (November 4, 2019): 323–34. http://dx.doi.org/10.1093/gji/ggz441.
Full textKatz, Justin, Iosif Pappas, Styliani Avraamidou, and Efstratios N. Pistikopoulos. "The Integration of Explicit MPC and ReLU based Neural Networks." IFAC-PapersOnLine 53, no. 2 (2020): 11350–55. http://dx.doi.org/10.1016/j.ifacol.2020.12.544.
Full textSchmidt-Hieber, Johannes. "Nonparametric regression using deep neural networks with ReLU activation function." Annals of Statistics 48, no. 4 (August 2020): 1875–97. http://dx.doi.org/10.1214/19-aos1875.
Full textOhn, Ilsang, and Yongdai Kim. "Smooth Function Approximation by Deep Neural Networks with General Activation Functions." Entropy 21, no. 7 (June 26, 2019): 627. http://dx.doi.org/10.3390/e21070627.
Full textOpschoor, Joost A. A., Philipp C. Petersen, and Christoph Schwab. "Deep ReLU networks and high-order finite element methods." Analysis and Applications 18, no. 05 (February 21, 2020): 715–70. http://dx.doi.org/10.1142/s0219530519410136.
Full textRyffel, Théo, Pierre Tholoniat, David Pointcheval, and Francis Bach. "AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing." Proceedings on Privacy Enhancing Technologies 2022, no. 1 (November 20, 2021): 291–316. http://dx.doi.org/10.2478/popets-2022-0015.
Full textBoopathy, Akhilan, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel. "CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3240–47. http://dx.doi.org/10.1609/aaai.v33i01.33013240.
Full textHatano, Naoya, Masahiro Ikeda, Isao Ishikawa, and Yoshihiro Sawano. "A Global Universality of Two-Layer Neural Networks with ReLU Activations." Journal of Function Spaces 2021 (September 17, 2021): 1–3. http://dx.doi.org/10.1155/2021/6637220.
Full textHerrera, Oscar, and Belém Priego. "Wavelets as activation functions in Neural Networks." Journal of Intelligent & Fuzzy Systems 42, no. 5 (March 31, 2022): 4345–55. http://dx.doi.org/10.3233/jifs-219225.
Full textAli, Mahmoud Emad Aldin, and Dinesh Kumar. "The Impact of Optimization Algorithms on The Performance of Face Recognition Neural Networks." Journal of Advanced Engineering and Computation 6, no. 4 (December 31, 2022): 248. http://dx.doi.org/10.55579/jaec.202264.370.
Full textYan, Zhiqi, Shisheng Zhong, Lin Lin, and Zhiquan Cui. "Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks." Mathematics 9, no. 17 (September 6, 2021): 2176. http://dx.doi.org/10.3390/math9172176.
Full textVeness, Joel, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, et al. "Gated Linear Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 10015–23. http://dx.doi.org/10.1609/aaai.v35i11.17202.
Full textInoue, Kenta. "Expressive Numbers of Two or More Hidden Layer ReLU Neural Networks." International Journal of Networking and Computing 10, no. 2 (2020): 293–307. http://dx.doi.org/10.15803/ijnc.10.2_293.
Full textPetersen, Philipp, and Felix Voigtlaender. "Optimal approximation of piecewise smooth functions using deep ReLU neural networks." Neural Networks 108 (December 2018): 296–330. http://dx.doi.org/10.1016/j.neunet.2018.08.019.
Full textOostwal, Elisa, Michiel Straat, and Michael Biehl. "Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation." Physica A: Statistical Mechanics and its Applications 564 (February 2021): 125517. http://dx.doi.org/10.1016/j.physa.2020.125517.
Full textSchmidt-Hieber, Johannes. "Rejoinder: “Nonparametric regression using deep neural networks with ReLU activation function”." Annals of Statistics 48, no. 4 (August 2020): 1916–21. http://dx.doi.org/10.1214/19-aos1931.
Full textVelasco, Lemuel Clark, John Frail Bongat, Ched Castillon, Jezreil Laurente, and Emily Tabanao. "Days-ahead water level forecasting using artificial neural networks for watersheds." Mathematical Biosciences and Engineering 20, no. 1 (2022): 758–74. http://dx.doi.org/10.3934/mbe.2023035.
Full text