Artigos de revistas sobre o tema "Generative sequence models"
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Wang, Yongkang, Xuan Liu, Feng Huang, Zhankun Xiong e Wen Zhang. "A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 1 (24 de março de 2024): 3–11. http://dx.doi.org/10.1609/aaai.v38i1.27749.
Texto completo da fonteWu, Zachary, Kadina E. Johnston, Frances H. Arnold e Kevin K. Yang. "Protein sequence design with deep generative models". Current Opinion in Chemical Biology 65 (dezembro de 2021): 18–27. http://dx.doi.org/10.1016/j.cbpa.2021.04.004.
Texto completo da fonteAkl, Hoda, Brooke Emison, Xiaochuan Zhao, Arup Mondal, Alberto Perez e Purushottam D. Dixit. "GENERALIST: A latent space based generative model for protein sequence families". PLOS Computational Biology 19, n.º 11 (27 de novembro de 2023): e1011655. http://dx.doi.org/10.1371/journal.pcbi.1011655.
Texto completo da fonteFeinauer, Christoph, Barthelemy Meynard-Piganeau e Carlo Lucibello. "Interpretable pairwise distillations for generative protein sequence models". PLOS Computational Biology 18, n.º 6 (23 de junho de 2022): e1010219. http://dx.doi.org/10.1371/journal.pcbi.1010219.
Texto completo da fonteWon, K. J., C. Saunders e A. Prügel-Bennett. "Evolving Fisher Kernels for Biological Sequence Classification". Evolutionary Computation 21, n.º 1 (março de 2013): 83–105. http://dx.doi.org/10.1162/evco_a_00065.
Texto completo da fonteLiu, Yitian, e Zhouhui Lian. "DeepCalliFont: Few-Shot Chinese Calligraphy Font Synthesis by Integrating Dual-Modality Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de março de 2024): 3774–82. http://dx.doi.org/10.1609/aaai.v38i4.28168.
Texto completo da fonteSafranchik, Esteban, Shiying Luo e Stephen Bach. "Weakly Supervised Sequence Tagging from Noisy Rules". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5570–78. http://dx.doi.org/10.1609/aaai.v34i04.6009.
Texto completo da fontePolceanu, Mihai, Julie Porteous, Alan Lindsay e Marc Cavazza. "Narrative Plan Generation with Self-Supervised Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 7 (18 de maio de 2021): 5984–92. http://dx.doi.org/10.1609/aaai.v35i7.16747.
Texto completo da fonteZhang, Zhiyuan, e Zhanshan Wang. "Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network". Mathematics 11, n.º 20 (19 de outubro de 2023): 4350. http://dx.doi.org/10.3390/math11204350.
Texto completo da fonteHawkins-Hooker, Alex, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen e David Bikard. "Generating functional protein variants with variational autoencoders". PLOS Computational Biology 17, n.º 2 (26 de fevereiro de 2021): e1008736. http://dx.doi.org/10.1371/journal.pcbi.1008736.
Texto completo da fonteTang, Fangfang, Mengyuan Ren, Xiaofan Li, Zhanglin Lin e Xiaofeng Yang. "Generating Novel and Soluble Class II Fructose-1,6-Bisphosphate Aldolase with ProteinGAN". Catalysts 13, n.º 12 (22 de novembro de 2023): 1457. http://dx.doi.org/10.3390/catal13121457.
Texto completo da fonteLu, Zhengdong, Todd K. Leen e Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals". Neural Computation 23, n.º 9 (setembro de 2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.
Texto completo da fontePhilip, Philemon, e Sidra Minhas. "A Brief Survey on Natural Language Processing Based Text Generation and Evaluation Techniques". VFAST Transactions on Software Engineering 10, n.º 3 (27 de setembro de 2022): 24–36. http://dx.doi.org/10.21015/vtse.v10i3.1104.
Texto completo da fonteBitard-Feildel, Tristan. "Navigating the amino acid sequence space between functional proteins using a deep learning framework". PeerJ Computer Science 7 (17 de setembro de 2021): e684. http://dx.doi.org/10.7717/peerj-cs.684.
Texto completo da fonteRamakers, Julius, Christopher Frederik Blum, Sabrina König, Stefan Harmeling e Markus Kollmann. "De novo prediction of RNA 3D structures with deep generative models". PLOS ONE 19, n.º 2 (15 de fevereiro de 2024): e0297105. http://dx.doi.org/10.1371/journal.pone.0297105.
Texto completo da fonteHazra, Debapriya, Mi-Ryung Kim e Yung-Cheol Byun. "Generative Adversarial Networks for Creating Synthetic Nucleic Acid Sequences of Cat Genome". International Journal of Molecular Sciences 23, n.º 7 (28 de março de 2022): 3701. http://dx.doi.org/10.3390/ijms23073701.
Texto completo da fonteZhang, Zhaohui, Lijun Yang, Ligong Chen, Qiuwen Liu, Ying Meng, Pengwei Wang e Maozhen Li. "A generative adversarial network–based method for generating negative financial samples". International Journal of Distributed Sensor Networks 16, n.º 2 (fevereiro de 2020): 155014772090705. http://dx.doi.org/10.1177/1550147720907053.
Texto completo da fonteShen, Xiaojuan, Tao Zeng, Nianhang Chen, Jiabo Li e Ruibo Wu. "NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer". Molecules 29, n.º 8 (19 de abril de 2024): 1867. http://dx.doi.org/10.3390/molecules29081867.
Texto completo da fonteXuan, Bona, Jin Li e Yafei Song. "SFCWGAN-BiTCN with Sequential Features for Malware Detection". Applied Sciences 13, n.º 4 (5 de fevereiro de 2023): 2079. http://dx.doi.org/10.3390/app13042079.
Texto completo da fonteTruong, Thanh-Dat, Chi Nhan Duong, Minh-Triet Tran, Ngan Le e Khoa Luu. "Fast Flow Reconstruction via Robust Invertible n × n Convolution". Future Internet 13, n.º 7 (8 de julho de 2021): 179. http://dx.doi.org/10.3390/fi13070179.
Texto completo da fonteYou, Yuyang, Xiaoyu Guo, Xuyang Zhong e Zhihong Yang. "A Few-Shot Learning-Based EEG and Stage Transition Sequence Generator for Improving Sleep Staging Performance". Biomedicines 10, n.º 12 (22 de novembro de 2022): 3006. http://dx.doi.org/10.3390/biomedicines10123006.
Texto completo da fonteWang, Zhenyi, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan e Changyou Chen. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 12281–88. http://dx.doi.org/10.1609/aaai.v34i07.6911.
Texto completo da fonteKim, Ha Young, e Dongsup Kim. "Prediction of mutation effects using a deep temporal convolutional network". Bioinformatics 36, n.º 7 (20 de novembro de 2019): 2047–52. http://dx.doi.org/10.1093/bioinformatics/btz873.
Texto completo da fonteHärkönen, Erik, Miika Aittala, Tuomas Kynkäänniemi, Samuli Laine, Timo Aila e Jaakko Lehtinen. "Disentangling random and cyclic effects in time-lapse sequences". ACM Transactions on Graphics 41, n.º 4 (julho de 2022): 1–13. http://dx.doi.org/10.1145/3528223.3530170.
Texto completo da fonteWilburn, Grey W., e Sean R. Eddy. "Remote homology search with hidden Potts models". PLOS Computational Biology 16, n.º 11 (30 de novembro de 2020): e1008085. http://dx.doi.org/10.1371/journal.pcbi.1008085.
Texto completo da fonteHu, Yijia. "Performance exploration of Generative Pre-trained Transformer-2 for lyrics generation". Applied and Computational Engineering 48, n.º 1 (19 de março de 2024): 53–60. http://dx.doi.org/10.54254/2755-2721/48/20241154.
Texto completo da fonteNguyen, Viet, Giang Vu, Tung Nguyen Thanh, Khoat Than e Toan Tran. "On Inference Stability for Diffusion Models". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de março de 2024): 14449–56. http://dx.doi.org/10.1609/aaai.v38i13.29359.
Texto completo da fonteLi, Shuyu, e Yunsick Sung. "Transformer-Based Seq2Seq Model for Chord Progression Generation". Mathematics 11, n.º 5 (23 de fevereiro de 2023): 1111. http://dx.doi.org/10.3390/math11051111.
Texto completo da fonteZAKI, NAZAR M., SAFAAI DERIS e ROSLI M. ILLIAS. "FEATURES EXTRACTION FOR PROTEIN HOMOLOGY DETECTION USING HIDDEN MARKOV MODELS COMBINING SCORES". International Journal of Computational Intelligence and Applications 04, n.º 01 (março de 2004): 1–12. http://dx.doi.org/10.1142/s1469026804001161.
Texto completo da fonteIsacchini, Giulio, Aleksandra M. Walczak, Thierry Mora e Armita Nourmohammad. "Deep generative selection models of T and B cell receptor repertoires with soNNia". Proceedings of the National Academy of Sciences 118, n.º 14 (1 de abril de 2021): e2023141118. http://dx.doi.org/10.1073/pnas.2023141118.
Texto completo da fonteWang, Chuantao, Xuexin Yang e Linkai Ding. "Imbalanced sentiment classification based on sequence generative adversarial nets". Journal of Intelligent & Fuzzy Systems 39, n.º 5 (19 de novembro de 2020): 7909–19. http://dx.doi.org/10.3233/jifs-201370.
Texto completo da fonteStokes, James, e John Terilla. "Probabilistic Modeling with Matrix Product States". Entropy 21, n.º 12 (17 de dezembro de 2019): 1236. http://dx.doi.org/10.3390/e21121236.
Texto completo da fonteWang, Xun, Changnan Gao, Peifu Han, Xue Li, Wenqi Chen, Alfonso Rodríguez Patón, Shuang Wang e Pan Zheng. "PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning". International Journal of Molecular Sciences 24, n.º 2 (6 de janeiro de 2023): 1146. http://dx.doi.org/10.3390/ijms24021146.
Texto completo da fonteWu, Shaohan, Jingfeng Xue, Yong Wang e Zixiao Kong. "Black-Box Evasion Attack Method Based on Confidence Score of Benign Samples". Electronics 12, n.º 11 (23 de maio de 2023): 2346. http://dx.doi.org/10.3390/electronics12112346.
Texto completo da fonteRuss, William P., Matteo Figliuzzi, Christian Stocker, Pierre Barrat-Charlaix, Michael Socolich, Peter Kast, Donald Hilvert et al. "An evolution-based model for designing chorismate mutase enzymes". Science 369, n.º 6502 (23 de julho de 2020): 440–45. http://dx.doi.org/10.1126/science.aba3304.
Texto completo da fonteChen, Yunjie, Marius Staring, Olaf M. Neve, Stephan R. Romeijn, Erik F. Hensen, Berit M. Verbist, Jelmer M. Wolterink e Qian Tao. "CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation". Machine Learning for Biomedical Imaging 2, Generative Models (9 de fevereiro de 2024): 657–85. http://dx.doi.org/10.59275/j.melba.2024-d61g.
Texto completo da fonteQi, Jingyuan, Minqian Liu, Ying Shen, Zhiyang Xu e Lifu Huang. "MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 17 (24 de março de 2024): 18888–96. http://dx.doi.org/10.1609/aaai.v38i17.29854.
Texto completo da fonteLiu, Danyang, Juntao Li, Meng-Hsuan Yu, Ziming Huang, Gongshen Liu, Dongyan Zhao e Rui Yan. "A Character-Centric Neural Model for Automated Story Generation". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 02 (3 de abril de 2020): 1725–32. http://dx.doi.org/10.1609/aaai.v34i02.5536.
Texto completo da fonteTubiana, Jérôme, Lucia Adriana-Lifshits, Michael Nissan, Matan Gabay, Inbal Sher, Marina Sova, Haim J. Wolfson e Maayan Gal. "Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions". PLOS Computational Biology 19, n.º 2 (2 de fevereiro de 2023): e1010874. http://dx.doi.org/10.1371/journal.pcbi.1010874.
Texto completo da fonteShen, Kevin. "Multi-world Model in Continual Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 21 (24 de março de 2024): 23757–59. http://dx.doi.org/10.1609/aaai.v38i21.30555.
Texto completo da fonteVaškevičius, Mantas, Jurgita Kapočiūtė-Dzikienė e Liudas Šlepikas. "Generative LLMs in Organic Chemistry: Transforming Esterification Reactions into Natural Language Procedures". Applied Sciences 13, n.º 24 (11 de dezembro de 2023): 13140. http://dx.doi.org/10.3390/app132413140.
Texto completo da fonteMarocco, Paolo, e Roberto Gigliucci. "An Investigation about Entailment and Narrative by AI Techniques (Generative Models)". Communication, Society and Media 3, n.º 4 (16 de novembro de 2020): p61. http://dx.doi.org/10.22158/csm.v3n4p61.
Texto completo da fonteLee, Sangho, Hayun Lee e Dongkun Shin. "Proxyformer: Nyström-Based Linear Transformer with Trainable Proxy Tokens". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de março de 2024): 13418–26. http://dx.doi.org/10.1609/aaai.v38i12.29244.
Texto completo da fonteLEE, CHAN-SU, e DIMITRIS SAMARAS. "ANALYSIS AND CONTROL OF FACIAL EXPRESSIONS USING DECOMPOSABLE NONLINEAR GENERATIVE MODELS". International Journal of Pattern Recognition and Artificial Intelligence 28, n.º 05 (31 de julho de 2014): 1456009. http://dx.doi.org/10.1142/s0218001414560096.
Texto completo da fonteLiu, Zuozhu, Thiparat Chotibut, Christopher Hillar e Shaowei Lin. "Biologically Plausible Sequence Learning with Spiking Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 02 (3 de abril de 2020): 1316–23. http://dx.doi.org/10.1609/aaai.v34i02.5487.
Texto completo da fonteZhou, Kun, Wenyong Wang, Teng Hu e Kai Deng. "Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks". Sensors 20, n.º 24 (16 de dezembro de 2020): 7211. http://dx.doi.org/10.3390/s20247211.
Texto completo da fonteWang, Yi. "Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks". PLOS ONE 18, n.º 5 (8 de maio de 2023): e0285496. http://dx.doi.org/10.1371/journal.pone.0285496.
Texto completo da fonteLi, Longyuan, Jihai Zhang, Junchi Yan, Yaohui Jin, Yunhao Zhang, Yanjie Duan e Guangjian Tian. "Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de maio de 2021): 8420–28. http://dx.doi.org/10.1609/aaai.v35i10.17023.
Texto completo da fonteMurad, Taslim, Sarwan Ali e Murray Patterson. "Exploring the Potential of GANs in Biological Sequence Analysis". Biology 12, n.º 6 (14 de junho de 2023): 854. http://dx.doi.org/10.3390/biology12060854.
Texto completo da fonteTrinquier, Jeanne, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi e Martin Weigt. "Efficient generative modeling of protein sequences using simple autoregressive models". Nature Communications 12, n.º 1 (4 de outubro de 2021). http://dx.doi.org/10.1038/s41467-021-25756-4.
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