Статті в журналах з теми "Deep neural networks (DNNs)"
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Galván, Edgar. "Neuroevolution in deep neural networks." ACM SIGEVOlution 14, no. 1 (April 2021): 3–7. http://dx.doi.org/10.1145/3460310.3460311.
Zhang, Lei, Shengyuan Zhou, Tian Zhi, Zidong Du, and Yunji Chen. "TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1319–26. http://dx.doi.org/10.1609/aaai.v33i01.33011319.
Díaz-Vico, David, Jesús Prada, Adil Omari, and José Dorronsoro. "Deep support vector neural networks." Integrated Computer-Aided Engineering 27, no. 4 (September 11, 2020): 389–402. http://dx.doi.org/10.3233/ica-200635.
Cai, Chenghao, Yanyan Xu, Dengfeng Ke, and Kaile Su. "Deep Neural Networks with Multistate Activation Functions." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/721367.
Verpoort, Philipp C., Alpha A. Lee, and David J. Wales. "Archetypal landscapes for deep neural networks." Proceedings of the National Academy of Sciences 117, no. 36 (August 25, 2020): 21857–64. http://dx.doi.org/10.1073/pnas.1919995117.
Xu, Xiangxiang, Shao-Lun Huang, Lizhong Zheng, and Gregory W. Wornell. "An Information Theoretic Interpretation to Deep Neural Networks." Entropy 24, no. 1 (January 17, 2022): 135. http://dx.doi.org/10.3390/e24010135.
Marrow, Scythia, Eric J. Michaud, and Erik Hoel. "Examining the Causal Structures of Deep Neural Networks Using Information Theory." Entropy 22, no. 12 (December 18, 2020): 1429. http://dx.doi.org/10.3390/e22121429.
Shu, Hai, and Hongtu Zhu. "Sensitivity Analysis of Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4943–50. http://dx.doi.org/10.1609/aaai.v33i01.33014943.
Nakamura, Kensuke, Bilel Derbel, Kyoung-Jae Won, and Byung-Woo Hong. "Learning-Rate Annealing Methods for Deep Neural Networks." Electronics 10, no. 16 (August 22, 2021): 2029. http://dx.doi.org/10.3390/electronics10162029.
Xu, Shenghe, Shivendra S. Panwar, Murali Kodialam, and T. V. Lakshman. "Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1684–91. http://dx.doi.org/10.1609/aaai.v34i02.5531.
Kutz, J. Nathan. "Deep learning in fluid dynamics." Journal of Fluid Mechanics 814 (January 31, 2017): 1–4. http://dx.doi.org/10.1017/jfm.2016.803.
Servais, Jason, and Ehsan Atoofian. "Adaptive Computation Reuse for Energy-Efficient Training of Deep Neural Networks." ACM Transactions on Embedded Computing Systems 20, no. 6 (November 30, 2021): 1–24. http://dx.doi.org/10.1145/3487025.
Jang, Hojin, Devin McCormack, and Frank Tong. "Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images." PLOS Biology 19, no. 12 (December 9, 2021): e3001418. http://dx.doi.org/10.1371/journal.pbio.3001418.
Aamir, Aisha, Minija Tamosiunaite, and Florentin Wörgötter. "Caffe2Unity: Immersive Visualization and Interpretation of Deep Neural Networks." Electronics 11, no. 1 (December 28, 2021): 83. http://dx.doi.org/10.3390/electronics11010083.
Jacobs, Robert A., and Christopher J. Bates. "Comparing the Visual Representations and Performance of Humans and Deep Neural Networks." Current Directions in Psychological Science 28, no. 1 (November 27, 2018): 34–39. http://dx.doi.org/10.1177/0963721418801342.
Zheng, Zhong, Xin Zhang, Jinxing Yu, Rui Guo, and Lili Zhangzhong. "Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees." Sensors 20, no. 4 (February 23, 2020): 1223. http://dx.doi.org/10.3390/s20041223.
Bassi, Pedro R. A. S., and Romis Attux. "FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces." Biomedical Physics & Engineering Express 8, no. 3 (April 8, 2022): 035018. http://dx.doi.org/10.1088/2057-1976/ac6300.
Sun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu, and Andrzej Ceglowski. "Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples." Mathematical Problems in Engineering 2020 (May 11, 2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.
Putra, Prasetia Utama, Keisuke Shima, and Koji Shimatani. "A deep neural network model for multi-view human activity recognition." PLOS ONE 17, no. 1 (January 7, 2022): e0262181. http://dx.doi.org/10.1371/journal.pone.0262181.
Luo, Yaoru, Guole Liu, Yuanhao Guo, and Ge Yang. "Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1908–16. http://dx.doi.org/10.1609/aaai.v36i2.20085.
Wang, Li-Na, Wenxue Liu, Xiang Liu, Guoqiang Zhong, Partha Pratim Roy, Junyu Dong, and Kaizhu Huang. "Compressing Deep Networks by Neuron Agglomerative Clustering." Sensors 20, no. 21 (October 23, 2020): 6033. http://dx.doi.org/10.3390/s20216033.
Zhang, Duzhen, Tielin Zhang, Shuncheng Jia, and Bo Xu. "Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 59–67. http://dx.doi.org/10.1609/aaai.v36i1.19879.
Grill-Spector, Kalanit, Kevin S. Weiner, Jesse Gomez, Anthony Stigliani, and Vaidehi S. Natu. "The functional neuroanatomy of face perception: from brain measurements to deep neural networks." Interface Focus 8, no. 4 (June 15, 2018): 20180013. http://dx.doi.org/10.1098/rsfs.2018.0013.
Harada, Akira, Shota Nishikawa, and Shoichi Yamada. "Deep Learning of the Eddington Tensor in Core-collapse Supernova Simulation." Astrophysical Journal 925, no. 2 (January 31, 2022): 117. http://dx.doi.org/10.3847/1538-4357/ac3998.
Cheng, Hao, Dongze Lian, Shenghua Gao, and Yanlin Geng. "Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification." Entropy 21, no. 5 (May 1, 2019): 456. http://dx.doi.org/10.3390/e21050456.
Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "Selective Poisoning Attack on Deep Neural Networks †." Symmetry 11, no. 7 (July 8, 2019): 892. http://dx.doi.org/10.3390/sym11070892.
Deng, Xiang, Yun Xiao, Bo Long, and Zhongfei Zhang. "Reducing Flipping Errors in Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6506–14. http://dx.doi.org/10.1609/aaai.v36i6.20603.
Deng, Hanming, Yang Hua, Tao Song, Zhengui Xue, Ruhui Ma, Neil Robertson, and Haibing Guan. "Reinforcing Neural Network Stability with Attractor Dynamics." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3765–72. http://dx.doi.org/10.1609/aaai.v34i04.5787.
Kanamura, Momomi, Kanata Suzuki, Yuki Suga, and Tetsuya Ogata. "Development of a Basic Educational Kit for Robotic System with Deep Neural Networks." Sensors 21, no. 11 (May 31, 2021): 3804. http://dx.doi.org/10.3390/s21113804.
Villalobos, Kimberly, Vilim Štih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki, and Xavier Boix. "Do Neural Networks for Segmentation Understand Insideness?" Neural Computation 33, no. 9 (August 19, 2021): 2511–49. http://dx.doi.org/10.1162/neco_a_01413.
Opschoor, 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.
Jin, Wei, Yaxing Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, and Jiliang Tang. "Adversarial Attacks and Defenses on Graphs." ACM SIGKDD Explorations Newsletter 22, no. 2 (January 17, 2021): 19–34. http://dx.doi.org/10.1145/3447556.3447566.
Krishnan, Gokul, Sumit K. Mandal, Chaitali Chakrabarti, Jae-Sun Seo, Umit Y. Ogras, and Yu Cao. "Impact of On-chip Interconnect on In-memory Acceleration of Deep Neural Networks." ACM Journal on Emerging Technologies in Computing Systems 18, no. 2 (April 30, 2022): 1–22. http://dx.doi.org/10.1145/3460233.
Cai, Jingyong, Masashi Takemoto, Yuming Qiu, and Hironori Nakajo. "Trigonometric Inference Providing Learning in Deep Neural Networks." Applied Sciences 11, no. 15 (July 21, 2021): 6704. http://dx.doi.org/10.3390/app11156704.
Cao, Yuan, and Quanquan Gu. "Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3349–56. http://dx.doi.org/10.1609/aaai.v34i04.5736.
Han, Pang Ying, Liew Yee Ping, Goh Fan Ling, Ooi Shih Yin, and Khoh Wee How. "Stacked deep analytic model for human activity recognition on a UCI HAR database." F1000Research 10 (October 15, 2021): 1046. http://dx.doi.org/10.12688/f1000research.73174.1.
Pang, Ying Han, Liew Yee Ping, Goh Fan Ling, Ooi Shih Yin, and Khoh Wee How. "Stacked deep analytic model for human activity recognition on a UCI HAR database." F1000Research 10 (April 1, 2022): 1046. http://dx.doi.org/10.12688/f1000research.73174.3.
Pang, Ying Han, Liew Yee Ping, Goh Fan Ling, Ooi Shih Yin, and Khoh Wee How. "Stacked deep analytic model for human activity recognition on a UCI HAR database." F1000Research 10 (February 18, 2022): 1046. http://dx.doi.org/10.12688/f1000research.73174.2.
Grant, Lauren L., and Clarissa S. Sit. "De novo molecular drug design benchmarking." RSC Medicinal Chemistry 12, no. 8 (2021): 1273–80. http://dx.doi.org/10.1039/d1md00074h.
Trimech, Imen Hamrouni, Ahmed Maalej, and Najoua Essoukri Ben Amara. "Facial Expression Recognition Using 3D Points Aware Deep Neural Network." Traitement du Signal 38, no. 2 (April 30, 2021): 321–30. http://dx.doi.org/10.18280/ts.380209.
Xue, Wanqi, Bo An, and Chai Kiat Yeo. "NSGZero: Efficiently Learning Non-exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4646–53. http://dx.doi.org/10.1609/aaai.v36i4.20389.
Jónsson, Hlynur, Giovanni Cherubini, and Evangelos Eleftheriou. "Convergence Behavior of DNNs with Mutual-Information-Based Regularization." Entropy 22, no. 7 (June 30, 2020): 727. http://dx.doi.org/10.3390/e22070727.
Ao, Ren, Zhang Tao, Wang Yuhao, Lin Sheng, Dong Peiyan, Chen Yen-kuang, Xie Yuan, and Wang Yanzhi. "DARB: A Density-Adaptive Regular-Block Pruning for Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5495–502. http://dx.doi.org/10.1609/aaai.v34i04.6000.
Elangovan, Reena, Shubham Jain, and Anand Raghunathan. "Ax-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration." ACM Transactions on Design Automation of Electronic Systems 27, no. 3 (May 31, 2022): 1–20. http://dx.doi.org/10.1145/3492733.
Nam, Woo-Jeoung, Shir Gur, Jaesik Choi, Lior Wolf, and Seong-Whan Lee. "Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2501–8. http://dx.doi.org/10.1609/aaai.v34i03.5632.
Hussain, Farhan, and Jechang Jeong. "Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3184840.
Venkat, Anand, Tharindu Rusira, Raj Barik, Mary Hall, and Leonard Truong. "SWIRL: High-performance many-core CPU code generation for deep neural networks." International Journal of High Performance Computing Applications 33, no. 6 (August 4, 2019): 1275–89. http://dx.doi.org/10.1177/1094342019866247.
Abraham, Lizy, Steven Davy, Muhammad Zawish, Rahul Mhapsekar, John A. Finn, and Patrick Moran. "Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks." Sensors 22, no. 6 (March 11, 2022): 2190. http://dx.doi.org/10.3390/s22062190.
Zhang, Xingwei, Xiaolong Zheng, and Wenji Mao. "Adversarial Perturbation Defense on Deep Neural Networks." ACM Computing Surveys 54, no. 8 (November 30, 2022): 1–36. http://dx.doi.org/10.1145/3465397.
Zhang, Xingwei, Xiaolong Zheng, and Wenji Mao. "Adversarial Perturbation Defense on Deep Neural Networks." ACM Computing Surveys 54, no. 8 (November 30, 2022): 1–36. http://dx.doi.org/10.1145/3465397.