Literatura científica selecionada sobre o tema "Deep Generatve Models"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Índice
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Deep Generatve Models".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Deep Generatve Models"
Mehmood, Rayeesa, Rumaan Bashir e Kaiser J. Giri. "Deep Generative Models: A Review". Indian Journal Of Science And Technology 16, n.º 7 (21 de fevereiro de 2023): 460–67. http://dx.doi.org/10.17485/ijst/v16i7.2296.
Texto completo da fonteRagoza, Matthew, Tomohide Masuda e David Ryan Koes. "Generating 3D molecules conditional on receptor binding sites with deep generative models". Chemical Science 13, n.º 9 (2022): 2701–13. http://dx.doi.org/10.1039/d1sc05976a.
Texto completo da fonteSalakhutdinov, Ruslan. "Learning Deep Generative Models". Annual Review of Statistics and Its Application 2, n.º 1 (10 de abril de 2015): 361–85. http://dx.doi.org/10.1146/annurev-statistics-010814-020120.
Texto completo da fontePartaourides, Harris, e Sotirios P. Chatzis. "Asymmetric deep generative models". Neurocomputing 241 (junho de 2017): 90–96. http://dx.doi.org/10.1016/j.neucom.2017.02.028.
Texto completo da fonteChangsheng Du, Changsheng Du, Yong Li Changsheng Du e Ming Wen Yong Li. "G-DCS: GCN-Based Deep Code Summary Generation Model". 網際網路技術學刊 24, n.º 4 (julho de 2023): 965–73. http://dx.doi.org/10.53106/160792642023072404014.
Texto completo da fonteWu, Han. "Face image generation and feature visualization using deep convolutional generative adversarial networks". Journal of Physics: Conference Series 2634, n.º 1 (1 de novembro de 2023): 012041. http://dx.doi.org/10.1088/1742-6596/2634/1/012041.
Texto completo da fonteBerrahal, Mohammed, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari e Idriss Idrissi. "Investigating the effectiveness of deep learning approaches for deep fake detection". Bulletin of Electrical Engineering and Informatics 12, n.º 6 (1 de dezembro de 2023): 3853–60. http://dx.doi.org/10.11591/eei.v12i6.6221.
Texto completo da fonteChe, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong e Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de maio de 2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.
Texto completo da fonteScurto, Hugo, Thomas Similowski, Samuel Bianchini e Baptiste Caramiaux. "Probing Respiratory Care With Generative Deep Learning". Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (28 de setembro de 2023): 1–34. http://dx.doi.org/10.1145/3610099.
Texto completo da fontePrakash Patil, Et al. "GAN-Enhanced Medical Image Synthesis: Augmenting CXR Data for Disease Diagnosis and Improving Deep Learning Performance". Journal of Electrical Systems 19, n.º 3 (25 de janeiro de 2024): 53–61. http://dx.doi.org/10.52783/jes.651.
Texto completo da fonteTeses / dissertações sobre o assunto "Deep Generatve Models"
Miao, Yishu. "Deep generative models for natural language processing". Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258.
Texto completo da fonteMisino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Texto completo da fonteNilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models". Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.
Texto completo da fonteDataaugmentering är en teknik som kan utföras på flera sätt för att förbättra träningen av diskriminativa modeller. De senaste framgångarna inom djupa generativa modeller har öppnat upp nya sätt att augmentera existerande dataset. I detta arbete har ett ramverk för augmentering av annoterade dataset med hjälp av djupa generativa modeller föreslagits. Utöver detta så har en metod för kvantitativ evaulering av kvaliteten hos genererade data set tagits fram. Med hjälp av detta ramverk har två dataset för pupillokalisering genererats med olika generativa modeller. Både väletablerade modeller och en ny modell utvecklad för detta syfte har testats. Den unika modellen visades både kvalitativt och kvantitativt att den genererade de bästa dataseten. Ett antal mindre experiment på standardiserade dataset visade exempel på fall där denna generativa modell kunde förbättra prestandan hos en existerande diskriminativ modell. Resultaten indikerar att generativa modeller kan användas för att augmentera eller ersätta existerande dataset vid träning av diskriminativa modeller.
Lindqvist, Niklas. "Automatic Question Paraphrasing in Swedish with Deep Generative Models". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294320.
Texto completo da fonteParafrasgenerering syftar på uppgiften att, utifrån en given mening eller text, automatiskt generera en parafras, det vill säga en annan text med samma betydelse. Parafrasgenerering är en grundläggande men ändå utmanande uppgift inom naturlig språkbehandling och används i en rad olika applikationer som informationssökning, konversionssystem, att besvara frågor givet en text etc. I den här studien undersöker vi problemet med parafrasgenerering av frågor på svenska genom att utvärdera två olika djupa generativa modeller som visat lovande resultat på parafrasgenerering av frågor på engelska. Den första modellen är en villkorsbaserad variationsautokodare (C-VAE). Den andra modellen är också en C-VAE men introducerar även en diskriminator vilket gör modellen till ett generativt motståndarnätverk (GAN). Förutom modellerna presenterade ovan, implementerades även en icke maskininlärningsbaserad metod som en baslinje. Modellerna utvärderades med både kvantitativa och kvalitativa mått inklusive grammatisk korrekthet och likvärdighet mellan parafras och originalfråga. Resultaten visar att de djupa generativa modellerna presterar bättre än baslinjemodellen på alla kvantitativa mätvärden. Vidare, visade the kvalitativa utvärderingen att de djupa generativa modellerna kunde generera grammatiskt korrekta frågor i större utsträckning än baslinjemodellen. Det var däremot ingen större skillnad i semantisk ekvivalens mellan parafras och originalfråga för de olika modellerna.
Gane, Georgiana Andreea. "Building generative models over discrete structures : from graphical models to deep learning". Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121611.
Texto completo da fonteCataloged from PDF version of thesis. Page 173 blank.
Includes bibliographical references (pages 159-172).
The goal of this thesis is to investigate generative models over discrete structures, such as binary grids, alignments or arbitrary graphs. We focused on developing models easy to sample from, and we approached the task from two broad perspectives: defining models via structured potential functions, and via neural network based decoders. In the first case, we investigated Perturbation Models, a family of implicit distributions where samples emerge through optimization of randomized potential functions. Designed explicitly for efficient sampling, Perturbation Models are strong candidates for building generative models over structures, and the leading open questions pertain to understanding the properties of the induced models and developing practical learning algorithms.
In this thesis, we present theoretical results showing that, in contrast to the more established Gibbs models, low-order potential functions, after undergoing randomization and maximization, lead to high-order dependencies in the induced distributions. Furthermore, while conditioning in Gibbs' distributions is straightforward, conditioning in Perturbation Models is typically not, but we theoretically characterize cases where the straightforward approach produces the correct results. Finally, we introduce a new Perturbation Models learning algorithm based on Inverse Combinatorial Optimization. We illustrate empirically both the induced dependencies and the inverse optimization approach, in learning tasks inspired by computer vision problems. In the second case, we sequentialize the structures, converting structure generation into a sequence of discrete decisions, to enable the use of sequential models.
We explore maximum likelihood training with step-wise supervision and continuous relaxations of the intermediate decisions. With respect to intermediate discrete representations, the main directions consist of using gradient estimators or designing continuous relaxations. We discuss these solutions in the context of unsupervised scene understanding with generative models. In particular, we asked whether a continuous relaxation of the counting problem also discovers the objects in an unsupervised fashion (given the increased training stability that continuous relaxations provide) and we proposed an approach based on Adaptive Computation Time (ACT) which achieves the desired result. Finally, we investigated the task of iterative graph generation. We proposed a variational lower-bound to the maximum likelihood objective, where the approximate posterior distribution renormalizes the prior distribution over local predictions which are plausible for the target graph.
For instance, the local predictions may be binary values indicating the presence or absence of an edge indexed by the given time step, for a canonical edge indexing chosen a-priori. The plausibility of each local prediction is assessed by solving a combinatorial optimization problem, and we discuss relevant approaches, including an induced sub-graph isomorphism-based algorithm for the generic graph generation case, and a polynomial algorithm for the special case of graph generation resulting from solving graph clustering tasks. In this thesis, we focused on the generic case, and we investigated the approximate posterior's relevance on synthetic graph datasets.
by Georgiana Andreea Gane.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Mescheder, Lars Morten [Verfasser]. "Stability and Expressiveness of Deep Generative Models / Lars Morten Mescheder". Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/1217249257/34.
Texto completo da fonteRastgoufard, Rastin. "Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models". ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/td/2486.
Texto completo da fonteDouwes, Constance. "On the Environmental Impact of Deep Generative Models for Audio". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS074.
Texto completo da fonteIn this thesis, we investigate the environmental impact of deep learning models for audio generation and we aim to put computational cost at the core of the evaluation process. In particular, we focus on different types of deep learning models specialized in raw waveform audio synthesis. These models are now a key component of modern audio systems, and their use has increased significantly in recent years. Their flexibility and generalization capabilities make them powerful tools in many contexts, from text-to-speech synthesis to unconditional audio generation. However, these benefits come at the cost of expensive training sessions on large amounts of data, operated on energy-intensive dedicated hardware, which incurs large greenhouse gas emissions. The measures we use as a scientific community to evaluate our work are at the heart of this problem. Currently, deep learning researchers evaluate their works primarily based on improvements in accuracy, log-likelihood, reconstruction, or opinion scores, all of which overshadow the computational cost of generative models. Therefore, we propose using a new methodology based on Pareto optimality to help the community better evaluate their work's significance while bringing energy footprint -- and in fine carbon emissions -- at the same level of interest as the sound quality. In the first part of this thesis, we present a comprehensive report on the use of various evaluation measures of deep generative models for audio synthesis tasks. Even though computational efficiency is increasingly discussed, quality measurements are the most commonly used metrics to evaluate deep generative models, while energy consumption is almost never mentioned. Therefore, we address this issue by estimating the carbon cost of training generative models and comparing it to other noteworthy carbon costs to demonstrate that it is far from insignificant. In the second part of this thesis, we propose a large-scale evaluation of pervasive neural vocoders, which are a class of generative models used for speech generation, conditioned on mel-spectrogram. We introduce a multi-objective analysis based on Pareto optimality of both quality from human-based evaluation and energy consumption. Within this framework, we show that lighter models can perform better than more costly models. By proposing to rely on a novel definition of efficiency, we intend to provide practitioners with a decision basis for choosing the best model based on their requirements. In the last part of the thesis, we propose a method to reduce the inference costs of neural vocoders, based on quantizated neural networks. We show a significant gain on the memory size and give some hints for the future use of these models on embedded hardware. Overall, we provide keys to better understand the impact of deep generative models for audio synthesis as well as a new framework for developing models while accounting for their environmental impact. We hope that this work raises awareness on the need to investigate energy-efficient models simultaneously with high perceived quality
Patsanis, Alexandros. "Network Anomaly Detection and Root Cause Analysis with Deep Generative Models". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397367.
Texto completo da fonteAlabdallah, Abdallah. "Human Understandable Interpretation of Deep Neural Networks Decisions Using Generative Models". Thesis, Högskolan i Halmstad, Halmstad Embedded and Intelligent Systems Research (EIS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41035.
Texto completo da fonteLivros sobre o assunto "Deep Generatve Models"
Mukhopadhyay, Anirban, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu e Yixuan Yuan, eds. Deep Generative Models. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2.
Texto completo da fonteMukhopadhyay, Anirban, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu e Yixuan Yuan, eds. Deep Generative Models. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53767-7.
Texto completo da fonteEngelhardt, Sandy, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman e Yuan Xue, eds. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5.
Texto completo da fonteAli, Hazrat, Mubashir Husain Rehmani e Zubair Shah, eds. Advances in Deep Generative Models for Medical Artificial Intelligence. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46341-9.
Texto completo da fonteBongard, Josh. Modeling self and others. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0011.
Texto completo da fonteMukhopadhyay, Anirban, Dajiang Zhu, Sandy Engelhardt, Ilkay Oksuz e Yixuan Yuan. Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, 2022.
Encontre o texto completo da fonteMukhopadhyay, Anirban, Dajiang Zhu, Sandy Engelhardt, Ilkay Oksuz e Yixuan Yuan. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. Springer International Publishing AG, 2021.
Encontre o texto completo da fonteConstantinesco, Thomas. Writing Pain in the Nineteenth-Century United States. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192855596.001.0001.
Texto completo da fonteJoho, Tobias. Thucydides, Epic, and Tragedy. Editado por Sara Forsdyke, Edith Foster e Ryan Balot. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199340385.013.40.
Texto completo da fonteAguayo, Angela J. Documentary Resistance. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190676216.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Deep Generatve Models"
Vasudevan, Shriram K., Sini Raj Pulari e Subashri Vasudevan. "Generative Models". In Deep Learning, 209–25. New York: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003185635-9.
Texto completo da fonteCalin, Ovidiu. "Generative Models". In Deep Learning Architectures, 591–609. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3_19.
Texto completo da fonteTomczak, Jakub M. "Autoregressive Models". In Deep Generative Modeling, 13–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_2.
Texto completo da fonteTomczak, Jakub M. "Energy-Based Models". In Deep Generative Modeling, 143–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_6.
Texto completo da fonteTomczak, Jakub M. "Flow-Based Models". In Deep Generative Modeling, 27–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_3.
Texto completo da fonteTomczak, Jakub M. "Latent Variable Models". In Deep Generative Modeling, 57–127. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_4.
Texto completo da fonteSchön, Julian, Raghavendra Selvan e Jens Petersen. "Interpreting Latent Spaces of Generative Models for Medical Images Using Unsupervised Methods". In Deep Generative Models, 24–33. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_3.
Texto completo da fonteMensing, Daniel, Jochen Hirsch, Markus Wenzel e Matthias Günther. "3D (c)GAN for Whole Body MR Synthesis". In Deep Generative Models, 97–105. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_10.
Texto completo da fonteDietrichstein, Marc, David Major, Martin Trapp, Maria Wimmer, Dimitrios Lenis, Philip Winter, Astrid Berg, Theresa Neubauer e Katja Bühler. "Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans". In Deep Generative Models, 77–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_8.
Texto completo da fonteTrullo, Roger, Quoc-Anh Bui, Qi Tang e Reza Olfati-Saber. "Image Translation Based Nuclei Segmentation for Immunohistochemistry Images". In Deep Generative Models, 87–96. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_9.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Deep Generatve Models"
Han, Tian, Jiawen Wu e Ying Nian Wu. "Replicating Active Appearance Model by Generator Network". In 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.2018/305.
Texto completo da fonteMisra, Siddharth, Jungang Chen, Polina Churilova e Yusuf Falola. "Generative Artificial Intelligence for Geomodeling". In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23477-ms.
Texto completo da fonteLi, Chen, Chikashige Yamanaka, Kazuma Kaitoh e Yoshihiro Yamanishi. "Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules". In 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.
Texto completo da fonteChen, Wei, e Faez Ahmed. "PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs". In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22729.
Texto completo da fonteXiao, Chaowei, Bo Li, Jun-yan Zhu, Warren He, Mingyan Liu e Dawn Song. "Generating Adversarial Examples with Adversarial Networks". In 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.2018/543.
Texto completo da fonteOussidi, Achraf, e Azeddine Elhassouny. "Deep generative models: Survey". In 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2018. http://dx.doi.org/10.1109/isacv.2018.8354080.
Texto completo da fonteLiu, Bochao, Pengju Wang, Shikun Li, Dan Zeng e Shiming Ge. "Model Conversion via Differentially Private Data-Free Distillation". In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/243.
Texto completo da fonteCintas, Celia, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman e Pin-Yu Chen. "Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning". In 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.2022/683.
Texto completo da fonteÇelik, Mustafa, e Ahmet HaydarÖrnek. "GAN-Based Data Augmentation and Anonymization for Mask Classification". In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112315.
Texto completo da fonteLopez, Christian, Scarlett R. Miller e Conrad S. Tucker. "Human Validation of Computer vs Human Generated Design Sketches". In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85698.
Texto completo da fonteRelatórios de organizações sobre o assunto "Deep Generatve Models"
Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera e Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, dezembro de 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Texto completo da fonteHuang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen e Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), fevereiro de 2024. http://dx.doi.org/10.21079/11681/48221.
Texto completo da fontePascal Notin, Pascal Notin. Designing ultrastable carbonic anhydrase with deep generative models and high-throughput assays. Experiment, outubro de 2023. http://dx.doi.org/10.18258/57574.
Texto completo da fonteSadoune, Igor, Marcelin Joanis e Andrea Lodi. Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data. CIRANO, setembro de 2023. http://dx.doi.org/10.54932/lqog8430.
Texto completo da fonteBidier, S., U. Khristenko, A. Kodakkal, C. Soriano e R. Rossi. D7.4 Final report on Stochastic Optimization results. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.02.
Texto completo da fonteMalej, Matt, e Fengyan Shi. Suppressing the pressure-source instability in modeling deep-draft vessels with low under-keel clearance in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), maio de 2021. http://dx.doi.org/10.21079/11681/40639.
Texto completo da fonteMohammadi, N., D. Corrigan, A. A. Sappin e N. Rayner. Evidence for a Neoarchean to earliest-Paleoproterozoic mantle metasomatic event prior to formation of the Mesoproterozoic-age Strange Lake REE deposit, Newfoundland and Labrador, and Quebec, Canada. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330866.
Texto completo da fonteHuijser, MP, J. W. Duffield, C. Neher, A. P. Clevenger e T. Mcguire. Final Report 2022: Update and expansion of the WVC mitigation measures and their cost-benefit model. Nevada Department of Transportation, outubro de 2022. http://dx.doi.org/10.15788/ndot2022.10.
Texto completo da fonteMbani, Benson, Timm Schoening e Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, maio de 2023. http://dx.doi.org/10.3289/sw_2_2023.
Texto completo da fonteBuesseler, Buessele, Daniele Bianchi, Fei Chai, Jay T. Cullen, Margaret Estapa, Nicholas Hawco, Seth John et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, outubro de 2023. http://dx.doi.org/10.1575/1912/67120.
Texto completo da fonte