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Статті в журналах з теми "Neural Sequence Models"
Shi, Tian, Yaser Keneshloo, Naren Ramakrishnan, and Chandan K. Reddy. "Neural Abstractive Text Summarization with Sequence-to-Sequence Models." ACM/IMS Transactions on Data Science 2, no. 1 (January 3, 2021): 1–37. http://dx.doi.org/10.1145/3419106.
Повний текст джерелаLiu, Bowen, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, and Vijay Pande. "Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models." ACS Central Science 3, no. 10 (September 5, 2017): 1103–13. http://dx.doi.org/10.1021/acscentsci.7b00303.
Повний текст джерелаPhua, Yeong Tsann, Sujata Navaratnam, Chon-Moy Kang, and Wai-Seong Che. "Sequence-to-sequence neural machine translation for English-Malay." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (June 1, 2022): 658. http://dx.doi.org/10.11591/ijai.v11.i2.pp658-665.
Повний текст джерелаDemeester, Thomas. "System Identification with Time-Aware Neural Sequence Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3757–64. http://dx.doi.org/10.1609/aaai.v34i04.5786.
Повний текст джерелаHalim, Calvin Janitra, and Kazuhiko Kawamoto. "2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting." Sensors 20, no. 15 (July 28, 2020): 4195. http://dx.doi.org/10.3390/s20154195.
Повний текст джерелаKalm, Kristjan, and Dennis Norris. "Sequence learning recodes cortical representations instead of strengthening initial ones." PLOS Computational Biology 17, no. 5 (May 24, 2021): e1008969. http://dx.doi.org/10.1371/journal.pcbi.1008969.
Повний текст джерелаTan, Zhixing, Jinsong Su, Boli Wang, Yidong Chen, and Xiaodong Shi. "Lattice-to-sequence attentional Neural Machine Translation models." Neurocomputing 284 (April 2018): 138–47. http://dx.doi.org/10.1016/j.neucom.2018.01.010.
Повний текст джерелаNam, Hyoungwook, Segwang Kim, and Kyomin Jung. "Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4626–33. http://dx.doi.org/10.1609/aaai.v33i01.33014626.
Повний текст джерелаYousuf, Hana, Michael Lahzi, Said A. Salloum, and Khaled Shaalan. "A systematic review on sequence-to-sequence learning with neural network and its models." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2315. http://dx.doi.org/10.11591/ijece.v11i3.pp2315-2326.
Повний текст джерелаBuckman, Jacob, and Graham Neubig. "Neural Lattice Language Models." Transactions of the Association for Computational Linguistics 6 (December 2018): 529–41. http://dx.doi.org/10.1162/tacl_a_00036.
Повний текст джерелаДисертації з теми "Neural Sequence Models"
Kann, Katharina [Verfasser], and Hinrich [Akademischer Betreuer] Schütze. "Neural sequence-to-sequence models for low-resource morphology / Katharina Kann ; Betreuer: Hinrich Schütze." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019. http://d-nb.info/1192663276/34.
Повний текст джерелаKhouzam, Bassem. "Neural networks as cellular computing models for temporal sequence processing." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0007/document.
Повний текст джерелаThe thesis proposes a sequence learning approach that uses the mechanism of fine grain self-organization. The manuscript initially starts by situating this effort in the perspective of contributing to the promotion of cellular computing paradigm in computer science. Computation within this paradigm is divided into a large number of elementary calculations carried out in parallel by computing cells, with information exchange between them.In addition to their fine grain nature, the cellular nature of such architectures lies in the spatial topology of the connections between cells that complies with to the constraints of the technological evolution of hardware in the future. In the manuscript, most of the distributed architecture known in computer science are examined following this perspective, to find that very few of them fall within the cellular paradigm.We are interested in the learning capacity of these architectures, because of the importance of this notion in the related domain of neural networks for example, without forgetting, however, that cellular systems are complex dynamical systems by construction.This inevitable dynamical component has motivated our focus on the learning of temporal sequences, for which we reviewed the different models in the domains of neural networks and self-organization maps.At the end, we proposed an architecture that contributes to the promotion of cellular computing in the sense that it exhibits self-organization properties employed in the extraction of a representation of a dynamical system states that provides the architecture with its entries, even if the latter are ambiguous such that they partially reflect the system state. We profited from an existing supercomputer to simulate complex architecture, that indeed exhibited a new emergent behavior. Based on these results we pursued a critical study that sets the perspective for future work
Cherla, S. "Neural probabilistic models for melody prediction, sequence labelling and classification." Thesis, City, University of London, 2016. http://openaccess.city.ac.uk/17444/.
Повний текст джерелаSarabi, Zahra. "Revealing the Positive Meaning of a Negation." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505158/.
Повний текст джерелаRehn, 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.
Повний текст джерелаIn 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
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.
Повний текст джерелаTaylor, Neill Richard. "Neural models of temporal sequences." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300844.
Повний текст джерелаCalvert, David. "A distance-based neural network model for sequence processing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0010/NQ30591.pdf.
Повний текст джерелаSchmidle, Wolfgang. "A model of neural sequence detectors for sentence processing." Thesis, University of Sunderland, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439973.
Повний текст джерелаHuang, Yiming. "Phoneme Recognition Using Neural Network and Sequence Learning Model." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1236027180.
Повний текст джерелаКниги з теми "Neural Sequence Models"
Mechanisms of implicit learning: Connectionist models of sequence processing. Cambridge, Mass: MIT Press, 1993.
Знайти повний текст джерелаKeeler, James David. Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memeory models. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1987.
Знайти повний текст джерелаSkelton, Kimberley, ed. Early Modern Spaces in Motion. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463725811.
Повний текст джерелаCleeremans, Axel. Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. MIT Press, 2019.
Знайти повний текст джерелаNieder, Andreas. Neuronal Correlates of Non-verbal Numerical Competence in Primates. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.027.
Повний текст джерелаЧастини книг з теми "Neural Sequence Models"
Ghatak, Abhijit. "Recurrent Neural Networks (RNN) or Sequence Models." In Deep Learning with R, 207–37. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5850-0_8.
Повний текст джерелаda Costa, Pablo, and Gustavo H. Paetzold. "Effective Sequence Labeling with Hybrid Neural-CRF Models." In Lecture Notes in Computer Science, 490–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99722-3_49.
Повний текст джерелаRatajczak, Martin, Sebastian Tschiatschek, and Franz Pernkopf. "Structured Regularizer for Neural Higher-Order Sequence Models." In Machine Learning and Knowledge Discovery in Databases, 168–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23528-8_11.
Повний текст джерелаHelali, Mossad, Thomas Kleinbauer, and Dietrich Klakow. "Assessing Unintended Memorization in Neural Discriminative Sequence Models." In Text, Speech, and Dialogue, 265–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58323-1_29.
Повний текст джерелаOnoda, Takashi. "Probabilistic Models Based Intrusion Detection Using Sequence Characteristics in Control System Communication." In Engineering Applications of Neural Networks, 155–64. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11071-4_15.
Повний текст джерелаMajumdar, Srijoni, Nachiketa Chatterjee, Partha Pratim Das, and Amlan Chakrabarti. "$$Dcube_{NN}$$: Tool for Dynamic Design Discovery from Multi-threaded Applications Using Neural Sequence Models." In Advanced Computing and Systems for Security: Volume 14, 75–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4294-4_6.
Повний текст джерелаXiong, Zhaohan, Aaqel Nalar, Kevin Jamart, Martin K. Stiles, Vadim V. Fedorov, and Jichao Zhao. "Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks." In Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 63–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39074-7_7.
Повний текст джерелаGrossberg, Stephen, and Rainer W. Paine. "Attentive Learning of Sequential Handwriting Movements: A Neural Network Model." In Sequence Learning, 349–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44565-x_16.
Повний текст джерелаSamura, Toshikazu, Motonobu Hattori, and Shun Ishizaki. "Sequence Disambiguation by Functionally Divided Hippocampal CA3 Model." In Neural Information Processing, 117–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893028_14.
Повний текст джерелаBastolla, Ugo, Markus Porto, H. Eduardo Roman, and Michele Vendruscolo. "The Structurally Constrained Neutral Model of Protein Evolution." In Structural Approaches to Sequence Evolution, 75–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-35306-5_4.
Повний текст джерелаТези доповідей конференцій з теми "Neural Sequence Models"
Strobelt, Hendrik, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander Rush. "Debugging Sequence-to-Sequence Models with Seq2Seq-Vis." In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-5451.
Повний текст джерелаKonstas, Ioannis, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. "Neural AMR: Sequence-to-Sequence Models for Parsing and Generation." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1014.
Повний текст джерелаYao, Kaisheng, and Geoffrey Zweig. "Sequence-to-sequence neural net models for grapheme-to-phoneme conversion." In Interspeech 2015. ISCA: ISCA, 2015. http://dx.doi.org/10.21437/interspeech.2015-134.
Повний текст джерелаCintas, Celia, William Ogallo, Aisha Walcott, Sekou L. Remy, Victor Akinwande, and Samuel Osebe. "Towards neural abstractive clinical trial text summarization with sequence to sequence models." In 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2019. http://dx.doi.org/10.1109/ichi.2019.8904526.
Повний текст джерелаShen, Liang-Hsin, Pei-Lun Tai, Chao-Chung Wu, and Shou-De Lin. "Controlling Sequence-to-Sequence Models - A Demonstration on Neural-based Acrostic Generator." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-3008.
Повний текст джерелаMiller, Jason Rafe, and Donald A. Adjeroh. "Exploring Neural Network Models for LncRNA Sequence Identification." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313445.
Повний текст джерелаRaganato, Alessandro, Claudio Delli Bovi, and Roberto Navigli. "Neural Sequence Learning Models for Word Sense Disambiguation." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1120.
Повний текст джерелаSperber, Matthias, Graham Neubig, Jan Niehues, and Alex Waibel. "Neural Lattice-to-Sequence Models for Uncertain Inputs." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1145.
Повний текст джерелаYannakoudakis, Helen, Marek Rei, Øistein E. Andersen, and Zheng Yuan. "Neural Sequence-Labelling Models for Grammatical Error Correction." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1297.
Повний текст джерелаMohan, Devang S. Ram, Raphael Lenain, Lorenzo Foglianti, Tian Huey Teh, Marlene Staib, Alexandra Torresquintero, and Jiameng Gao. "Incremental Text to Speech for Neural Sequence-to-Sequence Models Using Reinforcement Learning." In Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-1822.
Повний текст джерелаЗвіти організацій з теми "Neural Sequence Models"
Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
Повний текст джерелаIrudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman, and Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, October 2004. http://dx.doi.org/10.32747/2004.7587221.bard.
Повний текст джерелаRafaeli, Ada, Russell Jurenka, and Chris Sander. Molecular characterisation of PBAN-receptors: a basis for the development and screening of antagonists against Pheromone biosynthesis in moth pest species. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7695862.bard.
Повний текст джерелаYaron, Zvi, Abigail Elizur, Martin Schreibman, and Yonathan Zohar. Advancing Puberty in the Black Carp (Mylopharyngodon piceus) and the Striped Bass (Morone saxatilis). United States Department of Agriculture, January 2000. http://dx.doi.org/10.32747/2000.7695841.bard.
Повний текст джерелаAltstein, Miriam, and Ronald Nachman. Rationally designed insect neuropeptide agonists and antagonists: application for the characterization of the pyrokinin/Pban mechanisms of action in insects. United States Department of Agriculture, October 2006. http://dx.doi.org/10.32747/2006.7587235.bard.
Повний текст джерела