Articoli di riviste sul tema "Deep generative modeling"
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Blaschke, Thomas, e Jürgen Bajorath. "Compound dataset and custom code for deep generative multi-target compound design". Future Science OA 7, n. 6 (luglio 2021): FSO715. http://dx.doi.org/10.2144/fsoa-2021-0033.
Joshi, Ameya, Minsu Cho, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian e Chinmay Hegde. "InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 04 (3 aprile 2020): 4377–84. http://dx.doi.org/10.1609/aaai.v34i04.5863.
Lai, Peter, e Feruza Amirkulova. "Acoustic metamaterial design using Conditional Wasserstein Generative Adversarial Networks". Journal of the Acoustical Society of America 151, n. 4 (aprile 2022): A253. http://dx.doi.org/10.1121/10.0011234.
Strokach, Alexey, e Philip M. Kim. "Deep generative modeling for protein design". Current Opinion in Structural Biology 72 (febbraio 2022): 226–36. http://dx.doi.org/10.1016/j.sbi.2021.11.008.
Lopez, Romain, Jeffrey Regier, Michael B. Cole, Michael I. Jordan e Nir Yosef. "Deep generative modeling for single-cell transcriptomics". Nature Methods 15, n. 12 (30 novembre 2018): 1053–58. http://dx.doi.org/10.1038/s41592-018-0229-2.
Lee, Ung-Gi, Sang-Hee Kang, Jong-Chan Lee, Seo-Yeon Choi, Ukmyung Choi e Cheol-Il Lim. "Development of Deep Learning-based Art Learning Support Tool: Using Generative Modeling". Korean Association for Educational Information and Media 26, n. 1 (31 marzo 2020): 207–36. http://dx.doi.org/10.15833/kafeiam.26.1.207.
Behnia, Farnaz, Dominik Karbowski e Vadim Sokolov. "Deep generative models for vehicle speed trajectories". Applied Stochastic Models in Business and Industry 39, n. 5 (settembre 2023): 701–19. http://dx.doi.org/10.1002/asmb.2816.
Janson, Giacomo, e Michael Feig. "Transferable deep generative modeling of intrinsically disordered protein conformations". PLOS Computational Biology 20, n. 5 (23 maggio 2024): e1012144. http://dx.doi.org/10.1371/journal.pcbi.1012144.
Zhang, Chun, Liangxu Xie, Xiaohua Lu, Rongzhi Mao, Lei Xu e Xiaojun Xu. "Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery". Molecules 29, n. 7 (27 marzo 2024): 1499. http://dx.doi.org/10.3390/molecules29071499.
Guliev, R. "Generative adversarial networks for modeling reservoirs with permeability anisotropy". IOP Conference Series: Materials Science and Engineering 1201, n. 1 (1 novembre 2021): 012066. http://dx.doi.org/10.1088/1757-899x/1201/1/012066.
Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che e Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 05 (3 aprile 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.
Drygala, C., B. Winhart, F. di Mare e H. Gottschalk. "Generative modeling of turbulence". Physics of Fluids 34, n. 3 (marzo 2022): 035114. http://dx.doi.org/10.1063/5.0082562.
Qiu, Cheng, Anam Abbas e Feruza Amirkulova. "Pentamode metamaterial design via generative modeling and deep learning". Journal of the Acoustical Society of America 151, n. 4 (aprile 2022): A255. http://dx.doi.org/10.1121/10.0011241.
Veres, Matthew, Medhat Moussa e Graham W. Taylor. "Modeling Grasp Motor Imagery Through Deep Conditional Generative Models". IEEE Robotics and Automation Letters 2, n. 2 (aprile 2017): 757–64. http://dx.doi.org/10.1109/lra.2017.2651945.
Zhang, Zaiwei, Zhenpei Yang, Chongyang Ma, Linjie Luo, Alexander Huth, Etienne Vouga e Qixing Huang. "Deep Generative Modeling for Scene Synthesis via Hybrid Representations". ACM Transactions on Graphics 39, n. 2 (14 aprile 2020): 1–21. http://dx.doi.org/10.1145/3381866.
Wang, Yong, Guoliang Li, Kaiyu Li e Haitao Yuan. "A Deep Generative Model for Trajectory Modeling and Utilization". Proceedings of the VLDB Endowment 16, n. 4 (dicembre 2022): 973–85. http://dx.doi.org/10.14778/3574245.3574277.
Martínez-Palomera, Jorge, Joshua S. Bloom e Ellianna S. Abrahams. "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters". Astronomical Journal 164, n. 6 (30 novembre 2022): 263. http://dx.doi.org/10.3847/1538-3881/ac9b3f.
Amirkulova, Feruza, Linwei Zhou, Anam Abbas, Peter Lai, Cheng Qiu e Tristan A. Shah. "Acoustic metamaterial design framework using deep learning and generative modeling". Journal of the Acoustical Society of America 151, n. 4 (aprile 2022): A253. http://dx.doi.org/10.1121/10.0011233.
Wang, Wei-Ching. "Sound localization via deep learning, generative modeling, and global optimization". Journal of the Acoustical Society of America 151, n. 4 (aprile 2022): A255. http://dx.doi.org/10.1121/10.0011240.
Yuan, Hao, Lei Cai, Zhengyang Wang, Xia Hu, Shaoting Zhang e Shuiwang Ji. "Computational modeling of cellular structures using conditional deep generative networks". Bioinformatics 35, n. 12 (6 novembre 2018): 2141–49. http://dx.doi.org/10.1093/bioinformatics/bty923.
Li, Shuai, e Hongjun Li. "Deep Generative Modeling Based on VAE-GAN for 3D Indoor Scene Synthesis". International Journal of Computer Games Technology 2023 (20 settembre 2023): 1–11. http://dx.doi.org/10.1155/2023/3368647.
Borysov, Stanislav S., Jeppe Rich e Francisco C. Pereira. "How to generate micro-agents? A deep generative modeling approach to population synthesis". Transportation Research Part C: Emerging Technologies 106 (settembre 2019): 73–97. http://dx.doi.org/10.1016/j.trc.2019.07.006.
Faez, Faezeh, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah e Hamid R. Rabiee. "SCGG: A deep structure-conditioned graph generative model". PLOS ONE 17, n. 11 (21 novembre 2022): e0277887. http://dx.doi.org/10.1371/journal.pone.0277887.
Johnsen, Martin, Oliver Brandt, Sergio Garrido e Francisco Pereira. "Population synthesis for urban resident modeling using deep generative models". Neural Computing and Applications 34, n. 6 (3 novembre 2021): 4677–92. http://dx.doi.org/10.1007/s00521-021-06622-2.
Borsoi, Ricardo Augusto, Tales Imbiriba e Jose Carlos Moreira Bermudez. "Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing". IEEE Transactions on Computational Imaging 6 (2020): 374–84. http://dx.doi.org/10.1109/tci.2019.2948726.
Zhang, Qing, Benqiang Wang, Xusheng Liang, Yizhen Li, Feng He e Yuexiang Hao. "Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks". Geofluids 2022 (5 settembre 2022): 1–10. http://dx.doi.org/10.1155/2022/9159242.
Bucher, Martin Juan José, Michael Anton Kraus, Romana Rust e Siyu Tang. "Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models". Automation in Construction 156 (dicembre 2023): 105128. http://dx.doi.org/10.1016/j.autcon.2023.105128.
Mishra, Akshansh, e Tarushi Pathak. "Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy". Indian Journal of Data Mining 1, n. 1 (10 maggio 2021): 1–6. http://dx.doi.org/10.35940/ijdm.a1603.051121.
Mishra, Akshansh, e Tarushi Pathak. "Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy". Indian Journal of Data Mining 1, n. 1 (10 maggio 2021): 1–6. http://dx.doi.org/10.54105/ijdm.a1603.051121.
Eguchi, Raphael R., Christian A. Choe e Po-Ssu Huang. "Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation". PLOS Computational Biology 18, n. 6 (27 giugno 2022): e1010271. http://dx.doi.org/10.1371/journal.pcbi.1010271.
Bianco, Michael J., Sharon Gannot, Efren Fernandez-Grande e Peter Gerstoft. "Semi-Supervised Source Localization in Reverberant Environments With Deep Generative Modeling". IEEE Access 9 (2021): 84956–70. http://dx.doi.org/10.1109/access.2021.3087697.
Bianco, Michael J., Sharon Gannot, Efren Fernandez-Grande e Peter Gerstoft. "Semi-supervised source localization in reverberant environments using deep generative modeling". Journal of the Acoustical Society of America 148, n. 4 (ottobre 2020): 2662. http://dx.doi.org/10.1121/1.5147419.
Pham, Tuan Minh, e Xiangyang Ju. "Simulation of Hadronic Interactions with Deep Generative Models". EPJ Web of Conferences 295 (2024): 09034. http://dx.doi.org/10.1051/epjconf/202429509034.
Yoshimori, Atsushi, Filip Miljković e Jürgen Bajorath. "Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling". Molecules 27, n. 2 (17 gennaio 2022): 570. http://dx.doi.org/10.3390/molecules27020570.
Mizginov, V. A., e S. Y. Danilov. "SYNTHETIC THERMAL BACKGROUND AND OBJECT TEXTURE GENERATION USING GEOMETRIC INFORMATION AND GAN". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (9 maggio 2019): 149–54. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-149-2019.
Lim, Jieon, e Weonyoung Joo. "Counterfactual image generation by disentangling data attributes with deep generative models". Communications for Statistical Applications and Methods 30, n. 6 (30 novembre 2023): 589–603. http://dx.doi.org/10.29220/csam.2023.30.6.589.
Saito, Yuki, Shinnosuke Takamichi e Hiroshi Saruwatari. "Perceptual-Similarity-Aware Deep Speaker Representation Learning for Multi-Speaker Generative Modeling". IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 1033–48. http://dx.doi.org/10.1109/taslp.2021.3059114.
Zhang, Jincheng, e Xiaowei Zhao. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network". Energy 238 (gennaio 2022): 121747. http://dx.doi.org/10.1016/j.energy.2021.121747.
Wang, Liwei, Yu-Chin Chan, Faez Ahmed, Zhao Liu, Ping Zhu e Wei Chen. "Deep generative modeling for mechanistic-based learning and design of metamaterial systems". Computer Methods in Applied Mechanics and Engineering 372 (dicembre 2020): 113377. http://dx.doi.org/10.1016/j.cma.2020.113377.
Mujahid, Omer, Ivan Contreras, Aleix Beneyto, Ignacio Conget, Marga Giménez e Josep Vehi. "Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models". Mathematics 10, n. 20 (12 ottobre 2022): 3741. http://dx.doi.org/10.3390/math10203741.
Zervou, Michaela, Effrosyni Doutsi, Yannis Pantazis e Panagiotis Tsakalides. "De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks". International Journal of Molecular Sciences 25, n. 10 (18 maggio 2024): 5506. http://dx.doi.org/10.3390/ijms25105506.
Donovan-Maiye, Rory M., Jackson M. Brown, Caleb K. Chan, Liya Ding, Calysta Yan, Nathalie Gaudreault, Julie A. Theriot, Mary M. Maleckar, Theo A. Knijnenburg e Gregory R. Johnson. "A deep generative model of 3D single-cell organization". PLOS Computational Biology 18, n. 1 (18 gennaio 2022): e1009155. http://dx.doi.org/10.1371/journal.pcbi.1009155.
Richie, Rodney C. "Basics of Artificial Intelligence (AI) Modeling". Journal of Insurance Medicine 51, n. 1 (28 maggio 2024): 35–40. http://dx.doi.org/10.17849/insm-51-1-35-40.1.
Ruan, Xiongtao, e Robert F. Murphy. "Evaluation of methods for generative modeling of cell and nuclear shape". Bioinformatics 35, n. 14 (7 dicembre 2018): 2475–85. http://dx.doi.org/10.1093/bioinformatics/bty983.
Xu, Yanwu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang e Kayhan Batmanghelich. "Generative-Discriminative Complementary Learning". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 04 (3 aprile 2020): 6526–33. http://dx.doi.org/10.1609/aaai.v34i04.6126.
Hou, Wenshu, Zicheng Liu, Junjie Deng e Jiacheng Wang. "How does AI create and recommend corresponding wallpapers based on the games played by users?" Applied and Computational Engineering 42, n. 1 (23 febbraio 2024): 147–55. http://dx.doi.org/10.54254/2755-2721/42/20230770.
Shchetinin, Eugeny Yu. "Brain-computer interaction modeling based on the stable diffusion model". Discrete and Continuous Models and Applied Computational Science 31, n. 3 (12 settembre 2023): 273–81. http://dx.doi.org/10.22363/2658-4670-2023-31-3-273-281.
Feldkamp, Niclas, Soeren Bergmann, Florian Conrad e Steffen Strassburger. "A Method Using Generative Adversarial Networks for Robustness Optimization". ACM Transactions on Modeling and Computer Simulation 32, n. 2 (30 aprile 2022): 1–22. http://dx.doi.org/10.1145/3503511.
Srikanth, M., e Bhanurangarao M. "Deep Learning Approaches for Predictive Modeling and Optimization of Metabolic Fluxes in Engineered Microorganism". Aug-Sept 2023, n. 35 (21 luglio 2023): 1–11. http://dx.doi.org/10.55529/ijrise.35.1.11.
Varga, Michal, Ján Jadlovský e Slávka Jadlovská. "Generative Enhancement of 3D Image Classifiers". Applied Sciences 10, n. 21 (22 ottobre 2020): 7433. http://dx.doi.org/10.3390/app10217433.