Auswahl der wissenschaftlichen Literatur zum Thema „Representation space / Latent space“
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Zeitschriftenartikel zum Thema "Representation space / Latent space"
Gat, Itai, Guy Lorberbom, Idan Schwartz und Tamir Hazan. „Latent Space Explanation by Intervention“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 1 (28.06.2022): 679–87. http://dx.doi.org/10.1609/aaai.v36i1.19948.
Der volle Inhalt der QuelleHuang, Yulei, Ziping Ma, Huirong Li und Jingyu Wang. „Dual Space Latent Representation Learning for Image Representation“. Mathematics 11, Nr. 11 (31.05.2023): 2526. http://dx.doi.org/10.3390/math11112526.
Der volle Inhalt der QuelleJin Dai, Jin Dai, und Zhifang Zheng Jin Dai. „Disentangling Representation of Variational Autoencoders Based on Cloud Models“. 電腦學刊 34, Nr. 6 (Dezember 2023): 001–14. http://dx.doi.org/10.53106/199115992023123406001.
Der volle Inhalt der QuelleNamatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš und Kaspars Sudars. „Towards Explainability of the Latent Space by Disentangled Representation Learning“. Information Technology and Management Science 26 (30.11.2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.
Der volle Inhalt der QuelleToledo-Marín, J. Quetzalcóatl, und James A. Glazier. „Using deep LSD to build operators in GANs latent space with meaning in real space“. PLOS ONE 18, Nr. 6 (29.06.2023): e0287736. http://dx.doi.org/10.1371/journal.pone.0287736.
Der volle Inhalt der QuelleSang, Neil. „Does Time Smoothen Space? Implications for Space-Time Representation“. ISPRS International Journal of Geo-Information 12, Nr. 3 (09.03.2023): 119. http://dx.doi.org/10.3390/ijgi12030119.
Der volle Inhalt der QuelleHeese, Raoul, Jochen Schmid, Michał Walczak und Michael Bortz. „Calibrated simplex-mapping classification“. PLOS ONE 18, Nr. 1 (17.01.2023): e0279876. http://dx.doi.org/10.1371/journal.pone.0279876.
Der volle Inhalt der QuelleYou, Cong-Zhe, Vasile Palade und Xiao-Jun Wu. „Robust structure low-rank representation in latent space“. Engineering Applications of Artificial Intelligence 77 (Januar 2019): 117–24. http://dx.doi.org/10.1016/j.engappai.2018.09.008.
Der volle Inhalt der QuelleBanyay, Gregory A., und Andrew S. Wixom. „Latent space representation method for structural acoustic assessments“. Journal of the Acoustical Society of America 155, Nr. 3_Supplement (01.03.2024): A141. http://dx.doi.org/10.1121/10.0027092.
Der volle Inhalt der QuelleShrivastava, Aditya Divyakant, und Douglas B. Kell. „FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space“. Molecules 26, Nr. 7 (03.04.2021): 2065. http://dx.doi.org/10.3390/molecules26072065.
Der volle Inhalt der QuelleDissertationen zum Thema "Representation space / Latent space"
Yao, Xu. „Latent representations for facial images and video editing“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT019.
Der volle Inhalt der QuelleLearning to edit facial images and videos is one of the most popular tasks in both academia and industrial research. This thesis addresses the problem of face editing for the special case of high-resolution images and videos.In this thesis, we develop deep learning-based methods to perform facial image editing. Specifically, we explore the task using the latent representations obtained from two types of deep neural networks: autoencoder-based models and generative adversarial networks. For each type of method, we consider a specific image editing problem and propose an effective solution that outperforms the state-of-the-art.The thesis contains two parts. In part I, we explore image editing tasks via the latent space of autoencoders. We first consider the style transfer task between photos and propose an effective algorithm that is built on a pair of autoencoder-based networks. Second, we study the face age editing task for high-resolution images, using an encoder-decoder architecture. The proposed network encodes a face image to age-invariant feature representations and learns a modulation vector corresponding to a target age. Our approach allows for fine-grained age editing on high-resolution images in a single unified model.In part II, we explore the editing task via the latent space of generative adversarial models (GANs). First, we consider the problem of facial attribute disentangled editing on synthetic and real images, by proposing a latent transformation network that acts in the latent space of a pre-trained GAN model. We also proposed a video manipulation pipeline, to generalize the editing result to videos. Second, we investigate the problem of GAN inversion -- the projection of a real image to the latent space of a pretrained GAN. In particular, we propose a feed-forward encoder, which encodes a given image to a feature code and a latent code in one pass. The proposed encoder is shown to be more accurate and stable for image and video inversion, meanwhile, maintaining good editing capacities
Prang, Mathieu. „Representation learning for symbolic music“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS489.
Der volle Inhalt der QuelleA key part in the recent success of deep language processing models lies in the ability to learn efficient word embeddings. These methods provide structured spaces of reduced dimensionality with interesting metric relationship properties. These, in turn, can be used as efficient input representations for handling more complex tasks. In this thesis, we focus on the task of learning embedding spaces for polyphonic music in the symbolic domain. To do so, we explore two different approaches.We introduce an embedding model based on a convolutional network with a novel type of self-modulated hierarchical attention, which is computed at each layer to obtain a hierarchical vision of musical information.Then, we propose another system based on VAEs, a type of auto-encoder that constrains the data distribution of the latent space to be close to a prior distribution. As polyphonic music information is very complex, the design of input representation is a crucial process. Hence, we introduce a novel representation of symbolic music data, which transforms a polyphonic score into a continuous signal.Finally, we show the potential of the resulting embedding spaces through the development of several creative applications used to enhance musical knowledge and expression, through tasks such as melodies modification or composer identification
Saund, Eric. „The Role of Knowledge in Visual Shape Representation“. Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/6833.
Der volle Inhalt der QuelleWanigasekara, Prashan. „Latent state space models for prediction“. Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106269.
Der volle Inhalt der QuelleCataloged from PDF version of thesis.
Includes bibliographical references (pages 95-98).
In this thesis, I explore a novel algorithm to model the joint behavior of multiple correlated signals. Our chosen example is the ECG (Electrocardiogram) and ABP (Arterial Blood Pressure) signals from patients in the ICU (Intensive Care Unit). I then use the generated models to predict blood pressure levels of ICU patients based on their historical ECG and ABP signals. The algorithm used is a variant of a Hidden Markov model. The new extension is termed as the Latent State Space Copula Model. In the novel Latent State Space Copula Modelthe ECG, ABP signals are considered to be correlated and are modeled using a bivariate Gaussian copula with Weibull marginals generated by a hidden state. We assume that there are hidden patient "states" that transition from one hidden state to another driving a joint ECG-ABP behavior. We estimate the parameters of the model using a novel Gibbs sampling approach. Using this model, we generate predictors that are the state probabilities at any given time step and use them to predict a patient's future health condition. The predictions made by the model are binary and detects whether the Mean arterial pressure(MAP) is going to be above or below a certain threshold at a future time step. Towards the end of the thesis I do a comparison between the new Latent State Space Copula Model and a state of the art Classical Discrete HMM. The Latent State Space Copula Model achieves an Area Under the ROC (AUROC) curve of .7917 for 5 states while the Classical Discrete HMM achieves an AUROC of .7609 for 5 states.
by Prashan Wanigasekara.
S.M. in Engineering and Management
Elguendouze, Sofiane. „Explainable Artificial Intelligence approaches for Image Captioning“. Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1003.
Der volle Inhalt der QuelleThe rapid advancement of image captioning models, driven by the integration of deep learning techniques that combine image and text modalities, has resulted in increasingly complex systems. However, these models often operate as black boxes, lacking the ability to provide transparent explanations for their decisions. This thesis addresses the explainability of image captioning systems based on Encoder-Attention-Decoder architectures, through four aspects. First, it explores the concept of the latent space, marking a departure from traditional approaches relying on the original representation space. Second, it introduces the notion of decisiveness, leading to the formulation of a new definition for the concept of component influence/decisiveness in the context of explainable image captioning, as well as a perturbation-based approach to capturing decisiveness. The third aspect aims to elucidate the factors influencing explanation quality, in particular the scope of explanation methods. Accordingly, latent-based variants of well-established explanation methods such as LRP and LIME have been developed, along with the introduction of a latent-centered evaluation approach called Latent Ablation. The fourth aspect of this work involves investigating what we call saliency and the representation of certain visual concepts, such as object quantity, at different levels of the captioning architecture
BIGGIO, MONICA. „Space in action: motor aspects of peripersonal space representation“. Doctoral thesis, Università degli studi di Genova, 2018. http://hdl.handle.net/11567/929746.
Der volle Inhalt der QuelleMohanadas, Rohin. „Discerning truck stop semantics through latent space clustering“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240598.
Der volle Inhalt der QuelleGPS-systemen för navigation har funnits i nästan tre årtionden och de kan numera även hittas i lokaliseringssystem. Scania har en flotta bestående av 300 000 uppkopplade fordon som skickar information om deras position till Scania. I den här masteruppsatsen används positionsinformationen från de Scaniafordon som klassificeras som stillastående. Denna klacificering bygger på rå positionsinformation som baserat på tid och rum inte får variera mer än vissa tröskelvärden och de beskriver därigenom platser där lastbilar har stannat. En oövervakad maskininlärningsmetod användes för att försöka förstå semantiken bakom dessa stillaståenden. Data från lastbilarna projiceras till ett lägre dimensionellt rum med hjälp av deep autoencoders och klustringen optimeras sedan fram i denna lägre dimension. Klustringen har i denna masteruppsats visat sig respresentativ för olika anledningar till stillastående lastbilar. Detta kan vara användbart för att förstå användarmönster men även förtransportsnavets användarstatistik.
Mathis, Alexander. „The representation of space in mammals“. Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-150029.
Der volle Inhalt der QuellePritchard, Annette. „Tourism representation, space and the power perspective“. Thesis, Manchester Metropolitan University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311204.
Der volle Inhalt der QuelleKelly, Michael C. „Efficient representation of adaptable virtual auditory space“. Thesis, University of York, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274510.
Der volle Inhalt der QuelleBücher zum Thema "Representation space / Latent space"
Maria, Balshaw, und Kennedy Liam 1946-, Hrsg. Urban space and representation. London: Pluto Press, 2000.
Den vollen Inhalt der Quelle findenBalshaw, Maria, und Kennedy Liam. Urban space and representation. London: Pluto Press, 2000.
Den vollen Inhalt der Quelle findenFernández, Juan A., und Javier González. Multi-Hierarchical Representation of Large-Scale Space. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9666-4.
Der volle Inhalt der QuelleBarnett, Clive. Culture and democracy: Media, space, and representation. Edinburgh: Edinburgh University Press, 2003.
Den vollen Inhalt der Quelle findenEwald, Björn Christian, und Carlos F. Noreña. The emperor and Rome: Space, representation, and ritual. Cambridge: Cambridge University Press, 2010.
Den vollen Inhalt der Quelle findenNaomi, Eilan, McCarthy Rosaleen A und Brewer Bill, Hrsg. Spatial representation: Problems in philosophy and psychology. Oxford [England]: Blackwell, 1993.
Den vollen Inhalt der Quelle findenNewcombe, Nora. Making space: The development of spatial representation and reasoning. Cambridge, Mass: MIT Press, 2000.
Den vollen Inhalt der Quelle findenRepresentation of space and domestic interiority in contemporary fiction. New Delhi, India: Authors Press, 2015.
Den vollen Inhalt der Quelle findenDavid, Wilson. Inventing black-on-black violence: Discourse, space, and representation. Syracuse, N.Y: Syracuse University Press, 2005.
Den vollen Inhalt der Quelle findenMaunganidze, Langtone. Representation and Materialization of Architecture and Space in Zimbabwe. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47761-4.
Der volle Inhalt der QuelleBuchteile zum Thema "Representation space / Latent space"
O’ Mahony, Niall, Anshul Awasthi, Joseph Walsh und Daniel Riordan. „Latent Space Cartography for Geometrically Enriched Latent Spaces“. In Communications in Computer and Information Science, 488–501. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_38.
Der volle Inhalt der QuelleAnandarajan, Murugan, Chelsey Hill und Thomas Nolan. „Semantic Space Representation and Latent Semantic Analysis“. In Practical Text Analytics, 77–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_6.
Der volle Inhalt der QuelleBentley, Peter J., Soo Ling Lim, Adam Gaier und Linh Tran. „Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders“. In Lecture Notes in Computer Science, 371–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_26.
Der volle Inhalt der QuelleAathreya, Saandeep, und Shaun Canavan. „Expression Recognition Using a Flow-Based Latent-Space Representation“. In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 151–65. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37745-7_11.
Der volle Inhalt der QuellePeychev, Momchil, Anian Ruoss, Mislav Balunović, Maximilian Baader und Martin Vechev. „Latent Space Smoothing for Individually Fair Representations“. In Lecture Notes in Computer Science, 535–54. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19778-9_31.
Der volle Inhalt der QuellePolderman, Jan Willem, und Jan C. Willems. „Elimination of Latent Variables and State Space Representations“. In Texts in Applied Mathematics, 201–40. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2953-5_6.
Der volle Inhalt der QuelleWang, Zhendong, Isak Samsten, Rami Mochaourab und Panagiotis Papapetrou. „Learning Time Series Counterfactuals via Latent Space Representations“. In Discovery Science, 369–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88942-5_29.
Der volle Inhalt der QuelleSchlemper, Jo, Ozan Oktay, Wenjia Bai, Daniel C. Castro, Jinming Duan, Chen Qin, Jo V. Hajnal und Daniel Rueckert. „Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning“. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 259–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00928-1_30.
Der volle Inhalt der QuelleLópez Diez, Paula, Jan Margeta, Khassan Diab, François Patou und Rasmus R. Paulsen. „Unsupervised Classification of Congenital Inner Ear Malformations Using DeepDiffusion for Latent Space Representation“. In Lecture Notes in Computer Science, 652–62. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43904-9_63.
Der volle Inhalt der QuelleBetancourt, Roland. „Extended in the Imagination: The Representation of Architectural Space in Byzantium“. In Architecture and Visual Culture in the Late Antique and Medieval Mediterranean, 105–24. Turnhout, Belgium: Brepols Publishers, 2021. http://dx.doi.org/10.1484/m.ama-eb.5.124437.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Representation space / Latent space"
Liu, Zitu, Jiawang Li, Yue Liu, Qun Liu, Guoyin Wang und Yike Guo. „Interpretability Latent Space Method: Exploiting Shapley Representation to Explain Latent Space“. In 2021 7th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2021. http://dx.doi.org/10.1109/bigdia53151.2021.9619687.
Der volle Inhalt der QuelleAttia, Mohamed, MennattAllah H. Attia, Julie Iskander, Khaled Saleh, Darius Nahavandi, Ahmed Abobakr, Mohammed Hossny und Saeid Nahavandi. „Fingerprint Synthesis Via Latent Space Representation“. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914499.
Der volle Inhalt der QuelleThuruthel, Thomas George, Kieran Gilday und Fumiya Iida. „Drift-Free Latent Space Representation for Soft Strain Sensors“. In 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft). IEEE, 2020. http://dx.doi.org/10.1109/robosoft48309.2020.9116021.
Der volle Inhalt der QuelleXu, Yi Tian, Yaqiao Li und David Meger. „Human Motion Prediction Via Pattern Completion in Latent Representation Space“. In 2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019. http://dx.doi.org/10.1109/crv.2019.00016.
Der volle Inhalt der QuelleDu, Sihua, Xiaoming Liu und Guan Yang. „Latent space knowledge representation enhancement for low resource machine translation“. In International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), herausgegeben von Lin Wang und Xiaogang Liu. SPIE, 2023. http://dx.doi.org/10.1117/12.2679623.
Der volle Inhalt der QuelleDos Santos, Anderson Carlos, und Valdir Grassi. „Pedestrian Trajectory Prediction with Pose Representation and Latent Space Variables“. In 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE). IEEE, 2021. http://dx.doi.org/10.1109/lars/sbr/wre54079.2021.9605473.
Der volle Inhalt der QuelleNerurkar, Pranav, Madhav Chandane und Sunil Bhirud. „Representation learning for social networks using Homophily based Latent Space Model“. In COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3312614.3312627.
Der volle Inhalt der QuelleBielawski, Romain, und Rufin VanRullen. „CLIP-based image captioning via unsupervised cycle-consistency in the latent space“. In Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.repl4nlp-1.22.
Der volle Inhalt der QuelleEgiazarian, Vage, Savva Ignatyev, Alexey Artemov, Oleg Voynov, Andrey Kravchenko, Youyi Zheng, Luiz Velho und Evgeny Burnaev. „Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds“. In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009102604210428.
Der volle Inhalt der QuelleSong, Dan, Carl Henrik Ek, Kai Huebner und Danica Kragic. „Embodiment-specific representation of robot grasping using graphical models and latent-space discretization“. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048145.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Representation space / Latent space"
Saiegh, Sebastián. Partisanship, Ideology, and Representation in Latin America. Inter-American Development Bank, August 2014. http://dx.doi.org/10.18235/0011656.
Der volle Inhalt der QuelleHoff, Peter D., Adrian E. Raftery und Mark S. Handcock. Latent Space Approaches to Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada458734.
Der volle Inhalt der QuelleZhytaryuk, Maryan, und Iryna Ivanova. ANTI-RUSSIAN NARRATIVES OF YURIY SHVETS (ON THE MATERIALS OF HIS AUTHOR’S YOUTUBE CHANNEL). Ivan Franko National University of Lviv, März 2024. http://dx.doi.org/10.30970/vjo.2024.54-55.12154.
Der volle Inhalt der QuelleXuping, Xie, Tang Qi und Tang Xianzhu. Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models. Office of Scientific and Technical Information (OSTI), Juni 2024. http://dx.doi.org/10.2172/2377685.
Der volle Inhalt der QuelleLubold, Shane, Arun Chandrasekhar und Tyler McCormick. Identifying the Latent Space Geometry of Network Models through Analysis of Curvature. Cambridge, MA: National Bureau of Economic Research, Dezember 2020. http://dx.doi.org/10.3386/w28273.
Der volle Inhalt der QuelleAganj, Iman, Christophe Lenglet und Guillermo Sapiro. ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction. Fort Belvoir, VA: Defense Technical Information Center, Mai 2010. http://dx.doi.org/10.21236/ada540656.
Der volle Inhalt der QuelleSolomon, A. D., M. D. Morris, J. Martin und M. Olszewski. Development of a simulation code for a latent heat thermal energy storage system in a space station. Office of Scientific and Technical Information (OSTI), April 1986. http://dx.doi.org/10.2172/5777340.
Der volle Inhalt der QuelleAggio, Carlos. 'Lady Leaders': The Case of Quotas for Women's Representation in Argentina. Inter-American Development Bank, Juli 2002. http://dx.doi.org/10.18235/0006873.
Der volle Inhalt der QuelleChervinchuk, Alina. THE CONCEPT OF ENEMY: REPRESENTATION IN THE UKRAINIAN MILITARY DOCUMENTARIES. Ivan Franko National University of Lviv, Februar 2021. http://dx.doi.org/10.30970/vjo.2021.49.11063.
Der volle Inhalt der QuelleKularatne, Dhanushka N., Subhrajit Bhattacharya und M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.
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