Littérature scientifique sur le sujet « Generative sequence models »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Generative sequence models ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Generative sequence models"
Wang, Yongkang, Xuan Liu, Feng Huang, Zhankun Xiong et Wen Zhang. « A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 1 (24 mars 2024) : 3–11. http://dx.doi.org/10.1609/aaai.v38i1.27749.
Texte intégralWu, Zachary, Kadina E. Johnston, Frances H. Arnold et Kevin K. Yang. « Protein sequence design with deep generative models ». Current Opinion in Chemical Biology 65 (décembre 2021) : 18–27. http://dx.doi.org/10.1016/j.cbpa.2021.04.004.
Texte intégralAkl, Hoda, Brooke Emison, Xiaochuan Zhao, Arup Mondal, Alberto Perez et Purushottam D. Dixit. « GENERALIST : A latent space based generative model for protein sequence families ». PLOS Computational Biology 19, no 11 (27 novembre 2023) : e1011655. http://dx.doi.org/10.1371/journal.pcbi.1011655.
Texte intégralFeinauer, Christoph, Barthelemy Meynard-Piganeau et Carlo Lucibello. « Interpretable pairwise distillations for generative protein sequence models ». PLOS Computational Biology 18, no 6 (23 juin 2022) : e1010219. http://dx.doi.org/10.1371/journal.pcbi.1010219.
Texte intégralWon, K. J., C. Saunders et A. Prügel-Bennett. « Evolving Fisher Kernels for Biological Sequence Classification ». Evolutionary Computation 21, no 1 (mars 2013) : 83–105. http://dx.doi.org/10.1162/evco_a_00065.
Texte intégralLiu, Yitian, et Zhouhui Lian. « DeepCalliFont : Few-Shot Chinese Calligraphy Font Synthesis by Integrating Dual-Modality Generative Models ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 4 (24 mars 2024) : 3774–82. http://dx.doi.org/10.1609/aaai.v38i4.28168.
Texte intégralSafranchik, Esteban, Shiying Luo et Stephen Bach. « Weakly Supervised Sequence Tagging from Noisy Rules ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 04 (3 avril 2020) : 5570–78. http://dx.doi.org/10.1609/aaai.v34i04.6009.
Texte intégralPolceanu, Mihai, Julie Porteous, Alan Lindsay et Marc Cavazza. « Narrative Plan Generation with Self-Supervised Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 7 (18 mai 2021) : 5984–92. http://dx.doi.org/10.1609/aaai.v35i7.16747.
Texte intégralZhang, Zhiyuan, et Zhanshan Wang. « Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network ». Mathematics 11, no 20 (19 octobre 2023) : 4350. http://dx.doi.org/10.3390/math11204350.
Texte intégralHawkins-Hooker, Alex, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen et David Bikard. « Generating functional protein variants with variational autoencoders ». PLOS Computational Biology 17, no 2 (26 février 2021) : e1008736. http://dx.doi.org/10.1371/journal.pcbi.1008736.
Texte intégralThèses sur le sujet "Generative sequence models"
Svensk, Gustav. « TDNet : A Generative Model for Taxi Demand Prediction ». Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.
Texte intégralGoodman, Genghis. « A Machine Learning Approach to Artificial Floorplan Generation ». UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.
Texte intégralTubiana, Jérôme. « Restricted Boltzmann machines : from compositional representations to protein sequence analysis ». Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
Texte intégralRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
Rehn, Martin. « Aspects of memory and representation in cortical computation ». Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4161.
Texte intégralIn this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.
QC 20100916
Shimagaki, Kai. « Advanced statistical modeling and variable selection for protein sequences ». Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS548.
Texte intégralOver the last few decades, protein sequencing techniques have been developed and continuous experiments have been done. Thanks to all of these efforts, nowadays, we have obtained more than two hundred million protein sequence data. In order to deal with such a huge amount of biological data, now, we need theories and technologies to extract information that we can understand and interpret.The key idea to resolve this problem is statistical physics and the state of the art of machine learning (ML). Statistical physics is a field of physics that can successfully describe many complex systems by extracting or reducing variables to be interpretable variables based on simple principles. ML, on the other hand, can represent data (such as reconstruction and classification) without assuming how the data was generated, i.e. physical phenomenon behind of data. In this dissertation, we report studies of protein sequence generative modeling and protein-residue contact predictions using statistical physics-inspired modeling and ML-oriented methods. In the first part, we review the general background of biology and genomics. Then we discuss statistical modelings for protein sequence. In particular, we review Direct Coupling Analysis (DCA), which is the core technology of our research. We also discuss the effects of higher-order statistics contained in protein sequences and introduces deep learning-based generative models as a model that can go beyond pairwise interaction
Adak, Bulent Mehmet. « Model-based Code Generation For The High Level Architecture Federates ». Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12609032/index.pdf.
Texte intégrals behavior model. The behavior model is based on Live Sequence Charts (LSCs), adopted as the behavioral specification formalism in the Federation Architecture Metamodel (FAMM). The FAMM is constructed conforming to metaGME, the meta-metamodel offered by Generic Modeling Environment (GME). FAMM serves as a formal language for describing federation architectures. We present a code generator that generates Java/AspectJ code directly from a federation architecture model. An objective is to help verify a federation architecture by testing it early in the development lifecycle. Another objective is to help developers construct complete federate applications. Our approach to achieve these objectives is aspect-oriented in that the code generated from the LSC in conjunction with the Federation Object Model (FOM) serves as the base code on which the computation logic is weaved as an aspect.
Kunst, Rafael. « Um injetor de erros aplicado à avaliação de desempenho do codificador de canal em redes IEEE 802.16 ». reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17800.
Texte intégralThe demand for providing multimedia services is increasing the development of wireless networks. Therefore, an important issue is to guarantee correct transmissions over channels that are affected by time and frequency variant conditions caused by physical impairments that lead to the occurrence of errors during the transmission. These errors are basically of two types: burst errors and random errors, typically modeled as Additive White Gaussian Noise (AWGN). Simulating the behavior of wireless channels affected by physical impairments has been subject of several investigations in the past years. Nevertheless, part of the current researches does not consider the occurrence of both errors at the same time. This approach may lead to imprecisions on the results obtained through simulations. This work proposea an error sequence generator which is able of generating both burst and AWGN error models. Moreover, the proposed model can generate hybrid errors sequences composed of both error types simultaneously. The proposed error sequence generator is applied to a case study that aims to evaluate the performance of the channel encoder of nomadic (fixed) and mobile IEEE 802.16 networks. In this context, we evaluate the error correction capability of FEC encoders which are mandatory according to IEEE 802.16 standard. Furthermore, we study the impact caused by the application of time diversity techniques on the transmission, considering scenarios affected by burst errors and AWGN. We also present a study about the theoretical throughput that can be reached by nomadic and mobile technologies. Finally, we discuss the technological advances brought by Orthogonal Frequency Division Multiple Access (OFDMA) channel multiplexing technique, which is employed in IEEE 802.16 mobile networks.
Künstner, Axel. « Birds as a Model for Comparative Genomic Studies ». Doctoral thesis, Uppsala universitet, Evolutionsbiologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-159766.
Texte intégralAlsafi, Radi Taha M. « Generation of complex recombinant fowlpox virus 9 (FP9) encoding simian immunodeficiency virus (SIVmac239) sequences as a model HIV vaccine candidate ». Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/generation-of-complex-recombinant-fowlpox-virus-9-fp9-encoding-simian-immunodeficiency-virus-sivmac239-sequences-as-a-model-hiv-vaccine-candidate(1a015762-8dc2-4153-a586-d7fab88b9658).html.
Texte intégralBlazejewski, Tomasz. « Generative Models for Synthetic Biology ». Thesis, 2020. https://doi.org/10.7916/d8-0xvy-cw79.
Texte intégralLivres sur le sujet "Generative sequence models"
Grigorev, Anatoliy. Methods and algorithms of data processing. ru : INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/22119.
Texte intégralGrigor'ev, Anatoliy, et Evgeniy Isaev. Methods and algorithms of data processing. ru : INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.
Texte intégralNarimani, Zahra, Ali Masoudi-Nejad et Nazanin Hosseinkhan. Next Generation Sequencing and Sequence Assembly : Methodologies and Algorithms. Springer, 2013.
Trouver le texte intégralNarimani, Zahra, Ali Masoudi-Nejad et Nazanin Hosseinkhan. Next Generation Sequencing and Sequence Assembly : Methodologies and Algorithms. Springer, 2013.
Trouver le texte intégralHaCohen, Ruth. Between Generation and Suspension. Sous la direction de Yael Kaduri. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199841547.013.13.
Texte intégralCruse, Holk, et Malte Schilling. Pattern generation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0024.
Texte intégralNewman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.
Texte intégralBanovic, Nikola, Jennifer Mankoff et Anind K. Dey. Computational Model of Human Routine Behaviours. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0015.
Texte intégralDutoit, Thierry, et Yannis Stylianou. Text-to-Speech Synthesis. Sous la direction de Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0017.
Texte intégralBylander, J. Superconducting Quantum Bits of Information—Coherence and Design Improvements. Sous la direction de A. V. Narlikar. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198738169.013.18.
Texte intégralChapitres de livres sur le sujet "Generative sequence models"
Theis, Julian, Ilia Mokhtarian et Houshang Darabi. « On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization ». Dans Lecture Notes in Business Information Processing, 281–93. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_21.
Texte intégralOssenberg-Engels, Julius, et Vicente Grau. « Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames ». Dans Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 109–18. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39074-7_12.
Texte intégralTrehan, Harshit, et Fabio Di Troia. « Fake Malware Generation Using HMM and GAN ». Dans Silicon Valley Cybersecurity Conference, 3–21. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96057-5_1.
Texte intégralChen, Xuguang, Hongbin Ma, Pujun Ji, Haiting Liu et Yan Liu. « Based on GAN Generating Chaotic Sequence ». Dans Communications in Computer and Information Science, 37–49. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_4.
Texte intégralPaaß, Gerhard, et Sven Giesselbach. « Foundation Models for Speech, Images, Videos, and Control ». Dans Artificial Intelligence : Foundations, Theory, and Algorithms, 313–82. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23190-2_7.
Texte intégralCamargo, Manuel, Marlon Dumas et Oscar González-Rojas. « Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning ». Dans Advanced Information Systems Engineering, 55–71. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07472-1_4.
Texte intégralVeitaite, Ilona, et Audrius Lopata. « Knowledge-Based UML Dynamic Models Generation from Enterprise Model in Hospital Information Management Process Example ». Dans Intelligent Systems for Sustainable Person-Centered Healthcare, 225–50. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79353-1_12.
Texte intégralTran, Quang Duy, et Fabio Di Troia. « Word Embeddings for Fake Malware Generation ». Dans Silicon Valley Cybersecurity Conference, 22–37. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_2.
Texte intégralVázquez-Domínguez, Irene, et Alejandro Garanto. « Considerations for Generating Humanized Mouse Models to Test Efficacy of Antisense Oligonucleotides ». Dans Methods in Molecular Biology, 267–79. New York, NY : Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2010-6_18.
Texte intégralBian, Jiawen, et Xiaobo Zhou. « Hidden Markov Models in Bioinformatics : SNV Inference from Next Generation Sequence ». Dans Hidden Markov Models, 123–33. New York, NY : Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7_9.
Texte intégralActes de conférences sur le sujet "Generative sequence models"
Shin, SungUk, Inseop Lee et Changhee Choi. « Anomaly Dataset Augmentation Using the Sequence Generative Models ». Dans 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00190.
Texte intégralZheng, Yanan, Lijie Wen, Jianmin Wang, Jun Yan et Lei Ji. « Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory ». Dans CIKM '17 : ACM Conference on Information and Knowledge Management. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3132847.3132952.
Texte intégralVychegzhanin, Sergey, Anastasia Kotelnikova, Alexander Sergeev et Evgeny Kotelnikov. « MaxProb : Controllable Story Generation from Storyline ». Dans INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-539-553.
Texte intégralZhou, Shen, et Tieyun Qian. « On the Strength of Sequence Labeling and Generative Models for Aspect Sentiment Triplet Extraction ». Dans Findings of the Association for Computational Linguistics : ACL 2023. Stroudsburg, PA, USA : Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.762.
Texte intégralLi, Chen, Chikashige Yamanaka, Kazuma Kaitoh et Yoshihiro Yamanishi. « Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/539.
Texte intégralTao, Chongyang, Shen Gao, Mingyue Shang, Wei Wu, Dongyan Zhao et Rui Yan. « Get The Point of My Utterance ! Learning Towards Effective Responses with Multi-Head Attention Mechanism ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/614.
Texte intégralYe, Zhenhui, Zhou Zhao, Yi Ren et Fei Wu. « SyntaSpeech : Syntax-Aware Generative Adversarial Text-to-Speech ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/620.
Texte intégralGuo, Zhendong, Wei Sun, Liming Song, Jun Li et Zhenping Feng. « Generative Transfer Optimization for Aerodynamic Design ». Dans GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-225.
Texte intégralXiao, Dongling, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu et Haifeng Wang. « ERNIE-GEN : An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation ». Dans Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California : International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/553.
Texte intégralAlbuquerque, Isabela, Joao Monteiro et Tiago Falk. « Generating Videos by Traversing Image Manifolds Learned by GANs ». Dans LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812036.
Texte intégralRapports d'organisations sur le sujet "Generative sequence models"
Cohen, Yuval, Christopher A. Cullis et Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, octobre 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
Texte intégralMerkulova, Yuliya. Система цифровых моделей - новая технология для баланса данных. Yuliya Merkulova, avril 2021. http://dx.doi.org/10.12731/er0430.26042021.
Texte intégralMbani, Benson, Timm Schoening et Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, mai 2023. http://dx.doi.org/10.3289/sw_2_2023.
Texte intégralDecleir, Cyril, Mohand-Saïd Hacid et Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.
Texte intégralMichelmore, Richard, Eviatar Nevo, Abraham Korol et Tzion Fahima. Genetic Diversity at Resistance Gene Clusters in Wild Populations of Lactuca. United States Department of Agriculture, février 2000. http://dx.doi.org/10.32747/2000.7573075.bard.
Texte intégralСоловйов, Володимир Миколайович, Vladimir Saptsin et Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, mars 2009. http://dx.doi.org/10.31812/0564/1131.
Texte intégralBurns, Malcom, et Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, septembre 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Texte intégralZhang, Hongbin B., David J. Bonfil et Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis : Chickpea as a Model. United States Department of Agriculture, mars 2003. http://dx.doi.org/10.32747/2003.7586464.bard.
Texte intégralGur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor et Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, janvier 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Texte intégralGafni, Yedidya, et Vitaly Citovsky. Molecular interactions of TYLCV capsid protein during assembly of viral particles. United States Department of Agriculture, avril 2007. http://dx.doi.org/10.32747/2007.7587233.bard.
Texte intégral