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Artykuły w czasopismach na temat "Deep Generatve Models"
Mehmood, Rayeesa, Rumaan Bashir i Kaiser J. Giri. "Deep Generative Models: A Review". Indian Journal Of Science And Technology 16, nr 7 (21.02.2023): 460–67. http://dx.doi.org/10.17485/ijst/v16i7.2296.
Pełny tekst źródłaRagoza, Matthew, Tomohide Masuda i David Ryan Koes. "Generating 3D molecules conditional on receptor binding sites with deep generative models". Chemical Science 13, nr 9 (2022): 2701–13. http://dx.doi.org/10.1039/d1sc05976a.
Pełny tekst źródłaSalakhutdinov, Ruslan. "Learning Deep Generative Models". Annual Review of Statistics and Its Application 2, nr 1 (10.04.2015): 361–85. http://dx.doi.org/10.1146/annurev-statistics-010814-020120.
Pełny tekst źródłaPartaourides, Harris, i Sotirios P. Chatzis. "Asymmetric deep generative models". Neurocomputing 241 (czerwiec 2017): 90–96. http://dx.doi.org/10.1016/j.neucom.2017.02.028.
Pełny tekst źródłaChangsheng Du, Changsheng Du, Yong Li Changsheng Du i Ming Wen Yong Li. "G-DCS: GCN-Based Deep Code Summary Generation Model". 網際網路技術學刊 24, nr 4 (lipiec 2023): 965–73. http://dx.doi.org/10.53106/160792642023072404014.
Pełny tekst źródłaWu, Han. "Face image generation and feature visualization using deep convolutional generative adversarial networks". Journal of Physics: Conference Series 2634, nr 1 (1.11.2023): 012041. http://dx.doi.org/10.1088/1742-6596/2634/1/012041.
Pełny tekst źródłaBerrahal, Mohammed, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari i Idriss Idrissi. "Investigating the effectiveness of deep learning approaches for deep fake detection". Bulletin of Electrical Engineering and Informatics 12, nr 6 (1.12.2023): 3853–60. http://dx.doi.org/10.11591/eei.v12i6.6221.
Pełny tekst źródłaChe, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong i Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 8 (18.05.2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.
Pełny tekst źródłaScurto, Hugo, Thomas Similowski, Samuel Bianchini i Baptiste Caramiaux. "Probing Respiratory Care With Generative Deep Learning". Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (28.09.2023): 1–34. http://dx.doi.org/10.1145/3610099.
Pełny tekst źródłaPrakash Patil, Et al. "GAN-Enhanced Medical Image Synthesis: Augmenting CXR Data for Disease Diagnosis and Improving Deep Learning Performance". Journal of Electrical Systems 19, nr 3 (25.01.2024): 53–61. http://dx.doi.org/10.52783/jes.651.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaMisino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Pełny tekst źródłaNilsson, 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.
Pełny tekst źródłaDataaugmentering 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.
Pełny tekst źródłaParafrasgenerering 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.
Pełny tekst źródłaCataloged 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.
Pełny tekst źródłaRastgoufard, Rastin. "Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models". ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/td/2486.
Pełny tekst źródłaDouwes, Constance. "On the Environmental Impact of Deep Generative Models for Audio". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS074.
Pełny tekst źródłaIn 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.
Pełny tekst źródłaAlabdallah, 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.
Pełny tekst źródłaKsiążki na temat "Deep Generatve Models"
Mukhopadhyay, Anirban, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu i Yixuan Yuan, red. Deep Generative Models. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2.
Pełny tekst źródłaMukhopadhyay, Anirban, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu i Yixuan Yuan, red. Deep Generative Models. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53767-7.
Pełny tekst źródłaEngelhardt, Sandy, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman i Yuan Xue, red. 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.
Pełny tekst źródłaAli, Hazrat, Mubashir Husain Rehmani i Zubair Shah, red. 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.
Pełny tekst źródłaBongard, Josh. Modeling self and others. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0011.
Pełny tekst źródłaMukhopadhyay, Anirban, Dajiang Zhu, Sandy Engelhardt, Ilkay Oksuz i Yixuan Yuan. Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, 2022.
Znajdź pełny tekst źródłaMukhopadhyay, Anirban, Dajiang Zhu, Sandy Engelhardt, Ilkay Oksuz i 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.
Znajdź pełny tekst źródłaConstantinesco, Thomas. Writing Pain in the Nineteenth-Century United States. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192855596.001.0001.
Pełny tekst źródłaJoho, Tobias. Thucydides, Epic, and Tragedy. Redaktorzy Sara Forsdyke, Edith Foster i Ryan Balot. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199340385.013.40.
Pełny tekst źródłaAguayo, Angela J. Documentary Resistance. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190676216.001.0001.
Pełny tekst źródłaCzęści książek na temat "Deep Generatve Models"
Vasudevan, Shriram K., Sini Raj Pulari i Subashri Vasudevan. "Generative Models". W Deep Learning, 209–25. New York: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003185635-9.
Pełny tekst źródłaCalin, Ovidiu. "Generative Models". W Deep Learning Architectures, 591–609. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3_19.
Pełny tekst źródłaTomczak, Jakub M. "Autoregressive Models". W Deep Generative Modeling, 13–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_2.
Pełny tekst źródłaTomczak, Jakub M. "Energy-Based Models". W Deep Generative Modeling, 143–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_6.
Pełny tekst źródłaTomczak, Jakub M. "Flow-Based Models". W Deep Generative Modeling, 27–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_3.
Pełny tekst źródłaTomczak, Jakub M. "Latent Variable Models". W Deep Generative Modeling, 57–127. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_4.
Pełny tekst źródłaSchön, Julian, Raghavendra Selvan i Jens Petersen. "Interpreting Latent Spaces of Generative Models for Medical Images Using Unsupervised Methods". W Deep Generative Models, 24–33. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_3.
Pełny tekst źródłaMensing, Daniel, Jochen Hirsch, Markus Wenzel i Matthias Günther. "3D (c)GAN for Whole Body MR Synthesis". W Deep Generative Models, 97–105. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_10.
Pełny tekst źródłaDietrichstein, Marc, David Major, Martin Trapp, Maria Wimmer, Dimitrios Lenis, Philip Winter, Astrid Berg, Theresa Neubauer i Katja Bühler. "Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans". W Deep Generative Models, 77–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_8.
Pełny tekst źródłaTrullo, Roger, Quoc-Anh Bui, Qi Tang i Reza Olfati-Saber. "Image Translation Based Nuclei Segmentation for Immunohistochemistry Images". W Deep Generative Models, 87–96. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_9.
Pełny tekst źródłaStreszczenia konferencji na temat "Deep Generatve Models"
Han, Tian, Jiawen Wu i Ying Nian Wu. "Replicating Active Appearance Model by Generator Network". W 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.
Pełny tekst źródłaMisra, Siddharth, Jungang Chen, Polina Churilova i Yusuf Falola. "Generative Artificial Intelligence for Geomodeling". W International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23477-ms.
Pełny tekst źródłaLi, Chen, Chikashige Yamanaka, Kazuma Kaitoh i Yoshihiro Yamanishi. "Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules". W 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.
Pełny tekst źródłaChen, Wei, i Faez Ahmed. "PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs". W 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.
Pełny tekst źródłaXiao, Chaowei, Bo Li, Jun-yan Zhu, Warren He, Mingyan Liu i Dawn Song. "Generating Adversarial Examples with Adversarial Networks". W 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.
Pełny tekst źródłaOussidi, Achraf, i Azeddine Elhassouny. "Deep generative models: Survey". W 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2018. http://dx.doi.org/10.1109/isacv.2018.8354080.
Pełny tekst źródłaLiu, Bochao, Pengju Wang, Shikun Li, Dan Zeng i Shiming Ge. "Model Conversion via Differentially Private Data-Free Distillation". W 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.
Pełny tekst źródłaCintas, Celia, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman i Pin-Yu Chen. "Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning". W 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.
Pełny tekst źródłaÇelik, Mustafa, i Ahmet HaydarÖrnek. "GAN-Based Data Augmentation and Anonymization for Mask Classification". W 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.
Pełny tekst źródłaLopez, Christian, Scarlett R. Miller i Conrad S. Tucker. "Human Validation of Computer vs Human Generated Design Sketches". W 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.
Pełny tekst źródłaRaporty organizacyjne na temat "Deep Generatve Models"
Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera i Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, grudzień 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Pełny tekst źródłaHuang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen i Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), luty 2024. http://dx.doi.org/10.21079/11681/48221.
Pełny tekst źródłaPascal Notin, Pascal Notin. Designing ultrastable carbonic anhydrase with deep generative models and high-throughput assays. Experiment, październik 2023. http://dx.doi.org/10.18258/57574.
Pełny tekst źródłaSadoune, Igor, Marcelin Joanis i Andrea Lodi. Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data. CIRANO, wrzesień 2023. http://dx.doi.org/10.54932/lqog8430.
Pełny tekst źródłaBidier, S., U. Khristenko, A. Kodakkal, C. Soriano i R. Rossi. D7.4 Final report on Stochastic Optimization results. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.02.
Pełny tekst źródłaMalej, Matt, i 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.), maj 2021. http://dx.doi.org/10.21079/11681/40639.
Pełny tekst źródłaMohammadi, N., D. Corrigan, A. A. Sappin i 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.
Pełny tekst źródłaHuijser, MP, J. W. Duffield, C. Neher, A. P. Clevenger i T. Mcguire. Final Report 2022: Update and expansion of the WVC mitigation measures and their cost-benefit model. Nevada Department of Transportation, październik 2022. http://dx.doi.org/10.15788/ndot2022.10.
Pełny tekst źródłaMbani, Benson, Timm Schoening i Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, maj 2023. http://dx.doi.org/10.3289/sw_2_2023.
Pełny tekst źródłaBuesseler, Buessele, Daniele Bianchi, Fei Chai, Jay T. Cullen, Margaret Estapa, Nicholas Hawco, Seth John i in. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, październik 2023. http://dx.doi.org/10.1575/1912/67120.
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