Relevant bibliographies by topics / Deep generative modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep generative modeling'

Author: Grafiati
Published: 22 June 2024
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Deep generative modeling.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Deep generative modeling"

1

Blaschke, Thomas, and Jürgen Bajorath. "Compound dataset and custom code for deep generative multi-target compound design." Future Science OA 7, no. 6 (2021): FSO715. http://dx.doi.org/10.2144/fsoa-2021-0033.

Full text
Abstract:
Aim: Generating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling. Methodology: The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated. Exemplary results & data: Proof-of-concept for deep generative MT-CPD design was established. Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available. Limitations & next steps: MT-CPD design via deep learning is sti
APA, Harvard, Vancouver, ISO, and other styles
2

Joshi, Ameya, Minsu Cho, Viraj Shah, et al. "InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4377–84. http://dx.doi.org/10.1609/aaai.v34i04.5863.

Full text
Abstract:
Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on seve
APA, Harvard, Vancouver, ISO, and other styles
3

Lai, Peter, and Feruza Amirkulova. "Acoustic metamaterial design using Conditional Wasserstein Generative Adversarial Networks." Journal of the Acoustical Society of America 151, no. 4 (2022): A253. http://dx.doi.org/10.1121/10.0011234.

Full text
Abstract:
This talk presents a method for generating planar configurations of scatterers with a reduced total scattering cross section (TSCS) by means of generative modeling and deep learning. The TSCS minimization via repeated forward modeling techniques, trial-error methods, and traditional optimization methods requires considerable computer resources and time. However, similar or better results can be achieved more efficiently by training a deep learning model to generate such optimized configurations producing low scattering effect. In this work, the Conditional Wasserstein Generative Adversarial Ne
APA, Harvard, Vancouver, ISO, and other styles
4

Komanduri, Aneesh. "Toward Causal Generative Modeling: From Representation to Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29275–76. https://doi.org/10.1609/aaai.v39i28.35215.

Full text
Abstract:
Deep learning has given rise to the field of representation learning, which aims to automatically extract rich semantics from data. However, there have been several challenges in the generalization capabilities of deep learning models. Recent works have highlighted beneficial properties of causal models that are desirable for learning robust models under distribution shifts. Thus, there has been a growing interest in causal representation learning for achieving generalizability in tasks involving reasoning and planning. The goal of my dissertation is to develop theoretical intuitions and pract
APA, Harvard, Vancouver, ISO, and other styles
5

Strokach, Alexey, and Philip M. Kim. "Deep generative modeling for protein design." Current Opinion in Structural Biology 72 (February 2022): 226–36. http://dx.doi.org/10.1016/j.sbi.2021.11.008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tomczak, Jakub M. "Deep Generative Modeling: From Probabilistic Framework to Generative AI." Entropy 27, no. 3 (2025): 238. https://doi.org/10.3390/e27030238.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lopez, Romain, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, and Nir Yosef. "Deep generative modeling for single-cell transcriptomics." Nature Methods 15, no. 12 (2018): 1053–58. http://dx.doi.org/10.1038/s41592-018-0229-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lee, Ung-Gi, Sang-Hee Kang, Jong-Chan Lee, Seo-Yeon Choi, Ukmyung Choi, and Cheol-Il Lim. "Development of Deep Learning-based Art Learning Support Tool: Using Generative Modeling." Korean Association for Educational Information and Media 26, no. 1 (2020): 207–36. http://dx.doi.org/10.15833/kafeiam.26.1.207.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Behnia, Farnaz, Dominik Karbowski, and Vadim Sokolov. "Deep generative models for vehicle speed trajectories." Applied Stochastic Models in Business and Industry 39, no. 5 (2023): 701–19. http://dx.doi.org/10.1002/asmb.2816.

Full text
Abstract:
AbstractGenerating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self‐driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed‐forward layers and are trained using advers
APA, Harvard, Vancouver, ISO, and other styles
10

Janson, Giacomo, and Michael Feig. "Transferable deep generative modeling of intrinsically disordered protein conformations." PLOS Computational Biology 20, no. 5 (2024): e1012144. http://dx.doi.org/10.1371/journal.pcbi.1012144.

Full text
Abstract:
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Deep generative modeling"

1

Skalic, Miha 1990. "Deep learning for drug design : modeling molecular shapes." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667503.

Full text
Abstract:
Designing novel drugs is a complex process which requires finding molecules in a vast chemical space that bind to a specific biomolecular target and have favorable physio-chemical properties. Machine learning methods can leverage previous data and use it for new predictions helping the processes of selection of molecule candidate without relying exclusively on experiments. Particularly, deep learning can be applied to extract complex patterns from simple representations. In this work we leverage deep learning to extract patterns from three-dimensional representations of molecules. We apply cl
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Tian Qi. "Deep kernel mean embeddings for generative modeling and feedforward style transfer." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62668.

Full text
Abstract:
The generation of data has traditionally been specified using hand-crafted algorithms. However, oftentimes the exact generative process is unknown while only a limited number of samples are observed. One such case is generating images that look visually similar to an exemplar image or as if coming from a distribution of images. We look into learning the generating process by constructing a similarity function that measures how close the generated image is to the target image. We discuss a framework in which the similarity function is specified by a pre-trained neural network without fi
APA, Harvard, Vancouver, ISO, and other styles
3

Brodie, Michael B. "Methods for Generative Adversarial Output Enhancement." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8763.

Full text
Abstract:
Generative Adversarial Networks (GAN) learn to synthesize novel samples for a given data distribution. While GANs can train on diverse data of various modalities, the most successful use cases to date apply GANs to computer vision tasks. Despite significant advances in training algorithms and network architectures, GANs still struggle to consistently generate high-quality outputs after training. We present a series of papers that improve GAN output inference qualitatively and quantitatively. The first chapter, Alpha Model Domination, addresses a related subfield of Multiple Choice Learning, wh
APA, Harvard, Vancouver, ISO, and other styles
4

Testolin, Alberto. "Modeling cognition with generative neural networks: The case of orthographic processing." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424619.

Full text
Abstract:
This thesis investigates the potential of generative neural networks to model cognitive processes. In contrast to many popular connectionist models, the computational framework adopted in this research work emphasizes the generative nature of cognition, suggesting that one of the primary goals of cognitive systems is to learn an internal model of the surrounding environment that can be used to infer causes and make predictions about the upcoming sensory information. In particular, we consider a powerful class of recurrent neural networks that learn probabilistic generative models from experien
APA, Harvard, Vancouver, ISO, and other styles
5

Yan, Guowei. "Interactive Modeling of Elastic Materials and Splashing Liquids." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1593098802306904.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sadok, Samir. "Audiovisual speech representation learning applied to emotion recognition." Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0003.

Full text
Abstract:
Les émotions sont vitales dans notre quotidien, devenant un centre d'intérêt majeur de la recherche en cours. La reconnaissance automatique des émotions a suscité beaucoup d'attention en raison de ses applications étendues dans des secteurs tels que la santé, l'éducation, le divertissement et le marketing. Ce progrès dans la reconnaissance émotionnelle est essentiel pour favoriser le développement de l'intelligence artificielle centrée sur l'humain. Les systèmes de reconnaissance des émotions supervisés se sont considérablement améliorés par rapport aux approches traditionnelles d’apprentissag
APA, Harvard, Vancouver, ISO, and other styles
7

Luc, Pauline. "Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM024/document.

Full text
Abstract:
Les modèles prédictifs ont le potentiel de permettre le transfert des succès récents en apprentissage par renforcement à de nombreuses tâches du monde réel, en diminuant le nombre d’interactions nécessaires avec l’environnement.La tâche de prédiction vidéo a attiré un intérêt croissant de la part de la communauté ces dernières années, en tant que cas particulier d’apprentissage prédictif dont les applications en robotique et dans les systèmes de navigations sont vastes.Tandis que les trames RGB sont faciles à obtenir et contiennent beaucoup d’information, elles sont extrêmement difficile à pré
APA, Harvard, Vancouver, ISO, and other styles
8

Ionascu, Beatrice. "Modelling user interaction at scale with deep generative methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.

Full text
Abstract:
Understanding how users interact with a company's service is essential for data-driven businesses that want to better cater to their users and improve their offering. By using a generative machine learning approach it is possible to model user behaviour and generate new data to simulate or recognize and explain typical usage patterns. In this work we introduce an approach for modelling users' interaction behaviour at scale in a client-service model. We propose a novel representation of multivariate time-series data as time pictures that express temporal correlations through spatial organizatio
APA, Harvard, Vancouver, ISO, and other styles
9

McClintick, Kyle W. "Training Data Generation Framework For Machine-Learning Based Classifiers." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.

Full text
Abstract:
In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and base
APA, Harvard, Vancouver, ISO, and other styles
10

Fang, Zhufeng. "USING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONS." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/193439.

Full text
Abstract:
We use geostatistical and pedotrasnfer functions to estimate the three-dimensional distributions of soil types and hydraulic properties in a relatively large volume of vadose zone underlying the Maricopa Agriculture Center near Phoenix, Arizona. Soil texture and bulk density data from the site are analyzed geostatistically to reveal the underlying stratigraphy as well as finer features of their three-dimensional variability in space. Such fine features are revealed by cokriging soil texture and water content measured prior to large-scale long-term infiltration experiments. Resultant estimates
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Deep generative modeling"

1

Tomczak, Jakub M. Deep Generative Modeling. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93158-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tomczak, Jakub M. Deep Generative Modeling. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64087-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ganem, Gabriel Loaiza. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience. [publisher not identified], 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yahi, Alexandre. Simulating drug responses in laboratory test time series with deep generative modeling. [publisher not identified], 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tomczak, Jakub. Deep Generative Modeling. Springer International Publishing AG, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Deep Generative Modeling. Springer International Publishing AG, 2024.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Deep Generative Modeling. Springer International Publishing AG, 2023.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hartnett, Gavin, Raffaele Vardavas, Lawrence Baker, et al. Deep Generative Modeling in Network Science with Applications to Public Policy Research. RAND Corporation, 2020. http://dx.doi.org/10.7249/wra843-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Bongard, Josh. Modeling self and others. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0011.

Full text
Abstract:
Embodied cognition is the view that intelligence arises out of the interaction between an agent’s body and its environment. Taking such a view generates novel scientific hypotheses about biological intelligence and opportunities for advancing artificial intelligence. In this chapter we review one such set of hypotheses regarding how a robot may generate models of self, and others, and then exploit those models to recover from damage or exhibit the rudiments of social cognition. This modeling of self and others draws mainly on three concepts from neuroscience and AI: forward and inverse models
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Deep generative modeling"

1

Tomczak, Jakub M. "Hybrid Modeling." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tomczak, Jakub M. "Hybrid Modeling." In Deep Generative Modeling. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64087-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tomczak, Jakub M. "Generative Adversarial Networks." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Tomczak, Jakub M. "Generative Adversarial Networks." In Deep Generative Modeling. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64087-2_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tomczak, Jakub M. "Deep Generative Modeling for Neural Compression." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tomczak, Jakub M. "Autoregressive Models." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Tomczak, Jakub M. "Energy-Based Models." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Tomczak, Jakub M. "Flow-Based Models." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Tomczak, Jakub M. "Why Deep Generative Modeling?" In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Tomczak, Jakub M. "Latent Variable Models." In Deep Generative Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Deep generative modeling"

1

Chelaru, Bogdan, and Catalin Onutu. "BIM APLICATIONS IN THE DESIGN OF DEEP FOUNDATIONS FOR WIND TURBINES." In SGEM International Multidisciplinary Scientific GeoConference 24. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/6.1/s27.50.

Full text
Abstract:
This paper outlines three key processes: 3D modeling based on the technical records, data mapping to objects in the Building Information Modelling (BIM) model, and data extraction from the model. Access to energy is a critical concern on the modern society, but equally important of how the energy is obtained. The methods used should be efficient enough to minimize adverse environmental impact, which holds significant importance in today�s energy production landscape. In the case of non-load-bearing subsoil, high structural loads or special requirements of the building, a deep foundation with d
APA, Harvard, Vancouver, ISO, and other styles
2

Rizia, Mst Mousumi, Chenchen Xu, Jennie Roberts, et al. "Understanding the Development of Disease in Radiology Scans of the Brain through Deep Generative Modelling." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822442.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Caccia, Lucas, Herke van Hoof, Aaron Courville, and Joelle Pineau. "Deep Generative Modeling of LiDAR Data." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968535.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Davoody, Amirhossein, Ananda S. Roy, and Sivakumar P. Mudanai. "Deep Generative Model for Device Variation Modeling." In 2023 International Electron Devices Meeting (IEDM). IEEE, 2023. http://dx.doi.org/10.1109/iedm45741.2023.10413830.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bianco, Michael J., Sharon Gannot, and Peter Gerstoft. "Semi-Supervised Source Localization with Deep Generative Modeling." In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231825.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Zhaoyu, Son P. Nguyen, Dong Xu, and Yi Shang. "Protein Loop Modeling Using Deep Generative Adversarial Network." In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. http://dx.doi.org/10.1109/ictai.2017.00166.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Fatir Ansari, Abdul, Jonathan Scarlett, and Harold Soh. "A Characteristic Function Approach to Deep Implicit Generative Modeling." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00750.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Liu, Yiding, Kaiqi Zhao, Gao Cong, and Zhifeng Bao. "Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00087.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ghimire, Sandesh, and Linwei Wang. "Deep Generative Modeling and Analysis of Cardiac Transmembrane Potential." In 2018 Computing in Cardiology Conference. Computing in Cardiology, 2018. http://dx.doi.org/10.22489/cinc.2018.075.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Dai, Mengyu, and Haibin Hang. "Manifold Matching via Deep Metric Learning for Generative Modeling." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00652.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Deep generative modeling"

1

Sadoune, Igor, Marcelin Joanis, and Andrea Lodi. Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data. CIRANO, 2023. http://dx.doi.org/10.54932/lqog8430.

Full text
Abstract:
We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for cr
APA, Harvard, Vancouver, ISO, and other styles
2

Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

Full text
Abstract:
One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
APA, Harvard, Vancouver, ISO, and other styles
3

Skyllingstad, Eric D. Next Generation Modeling for Deep Water Wave Breaking and Langmuir Circulation. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada498290.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Skyllingstad, Eric D. Next Generation Modeling for Deep Water Wave Breaking and Langmuir Circulation. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada534062.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Beaulieu, Stace E., Karen Stocks, and Leslie M. Smith. FAIR Data Training for Deep Ocean Early Career Researchers: Syllabus and slide presentations. Woods Hole Oceanographic Institution, 2024. http://dx.doi.org/10.1575/1912/67631.

Full text
Abstract:
It is essential for our next generation of leaders in deep ocean observing to gain knowledge and skills in research data management, including how to make data FAIR - Findable, Accessible, Interoperable, and Reusable. This educational package was developed as a virtual workshop series for Deep Ocean Early career Researchers (DOERs) with content tailored for the Deep Ocean Observing Strategy (DOOS), an international network of deep ocean observing, mapping, exploration, and modeling programs endorsed as a UN Ocean Decade Programme. Modules step through the research data lifecycle, starting with
APA, Harvard, Vancouver, ISO, and other styles
6

Buesseler, Buessele, Daniele Bianchi, Fei Chai, et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, 2023. http://dx.doi.org/10.1575/1912/67120.

Full text
Abstract:
We need a new way of talking about global warming. UN Secretary General António Guterres underscored this when he said the “era of global boiling” has arrived. Although we have made remarkable progress on a very complex problem over the past thirty years, we have a long way to go before we can keep the global temperature increase to below 2°C relative to the pre-industrial times. Climate models suggest that this next decade is critical if we are to avert the worst consequences of climate change. The world must continue to reduce greenhouse gas emissions, and find ways to adapt and build resili
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Deep generative modeling' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography