Literatura académica sobre el tema "Representation space / Latent space"
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Artículos de revistas sobre el tema "Representation space / Latent space"
Gat, Itai, Guy Lorberbom, Idan Schwartz y Tamir Hazan. "Latent Space Explanation by Intervention". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junio de 2022): 679–87. http://dx.doi.org/10.1609/aaai.v36i1.19948.
Texto completoHuang, Yulei, Ziping Ma, Huirong Li y Jingyu Wang. "Dual Space Latent Representation Learning for Image Representation". Mathematics 11, n.º 11 (31 de mayo de 2023): 2526. http://dx.doi.org/10.3390/math11112526.
Texto completoJin Dai, Jin Dai y Zhifang Zheng Jin Dai. "Disentangling Representation of Variational Autoencoders Based on Cloud Models". 電腦學刊 34, n.º 6 (diciembre de 2023): 001–14. http://dx.doi.org/10.53106/199115992023123406001.
Texto completoNamatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš y Kaspars Sudars. "Towards Explainability of the Latent Space by Disentangled Representation Learning". Information Technology and Management Science 26 (30 de noviembre de 2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.
Texto completoToledo-Marín, J. Quetzalcóatl y James A. Glazier. "Using deep LSD to build operators in GANs latent space with meaning in real space". PLOS ONE 18, n.º 6 (29 de junio de 2023): e0287736. http://dx.doi.org/10.1371/journal.pone.0287736.
Texto completoSang, Neil. "Does Time Smoothen Space? Implications for Space-Time Representation". ISPRS International Journal of Geo-Information 12, n.º 3 (9 de marzo de 2023): 119. http://dx.doi.org/10.3390/ijgi12030119.
Texto completoHeese, Raoul, Jochen Schmid, Michał Walczak y Michael Bortz. "Calibrated simplex-mapping classification". PLOS ONE 18, n.º 1 (17 de enero de 2023): e0279876. http://dx.doi.org/10.1371/journal.pone.0279876.
Texto completoYou, Cong-Zhe, Vasile Palade y Xiao-Jun Wu. "Robust structure low-rank representation in latent space". Engineering Applications of Artificial Intelligence 77 (enero de 2019): 117–24. http://dx.doi.org/10.1016/j.engappai.2018.09.008.
Texto completoBanyay, Gregory A. y Andrew S. Wixom. "Latent space representation method for structural acoustic assessments". Journal of the Acoustical Society of America 155, n.º 3_Supplement (1 de marzo de 2024): A141. http://dx.doi.org/10.1121/10.0027092.
Texto completoShrivastava, Aditya Divyakant y Douglas B. Kell. "FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space". Molecules 26, n.º 7 (3 de abril de 2021): 2065. http://dx.doi.org/10.3390/molecules26072065.
Texto completoTesis sobre el tema "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.
Texto completoLearning 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.
Texto completoA 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.
Texto completoWanigasekara, Prashan. "Latent state space models for prediction". Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106269.
Texto completoCataloged 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.
Texto completoThe 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.
Texto completoMohanadas, 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.
Texto completoGPS-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.
Texto completoPritchard, Annette. "Tourism representation, space and the power perspective". Thesis, Manchester Metropolitan University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311204.
Texto completoKelly, 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.
Texto completoLibros sobre el tema "Representation space / Latent space"
Maria, Balshaw y Kennedy Liam 1946-, eds. Urban space and representation. London: Pluto Press, 2000.
Buscar texto completoBalshaw, Maria y Kennedy Liam. Urban space and representation. London: Pluto Press, 2000.
Buscar texto completoFernández, Juan A. y Javier González. Multi-Hierarchical Representation of Large-Scale Space. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9666-4.
Texto completoBarnett, Clive. Culture and democracy: Media, space, and representation. Edinburgh: Edinburgh University Press, 2003.
Buscar texto completoEwald, Björn Christian y Carlos F. Noreña. The emperor and Rome: Space, representation, and ritual. Cambridge: Cambridge University Press, 2010.
Buscar texto completoNaomi, Eilan, McCarthy Rosaleen A y Brewer Bill, eds. Spatial representation: Problems in philosophy and psychology. Oxford [England]: Blackwell, 1993.
Buscar texto completoNewcombe, Nora. Making space: The development of spatial representation and reasoning. Cambridge, Mass: MIT Press, 2000.
Buscar texto completoRepresentation of space and domestic interiority in contemporary fiction. New Delhi, India: Authors Press, 2015.
Buscar texto completoDavid, Wilson. Inventing black-on-black violence: Discourse, space, and representation. Syracuse, N.Y: Syracuse University Press, 2005.
Buscar texto completoMaunganidze, 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.
Texto completoCapítulos de libros sobre el tema "Representation space / Latent space"
O’ Mahony, Niall, Anshul Awasthi, Joseph Walsh y Daniel Riordan. "Latent Space Cartography for Geometrically Enriched Latent Spaces". En 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.
Texto completoAnandarajan, Murugan, Chelsey Hill y Thomas Nolan. "Semantic Space Representation and Latent Semantic Analysis". En Practical Text Analytics, 77–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_6.
Texto completoBentley, Peter J., Soo Ling Lim, Adam Gaier y Linh Tran. "Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders". En Lecture Notes in Computer Science, 371–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_26.
Texto completoAathreya, Saandeep y Shaun Canavan. "Expression Recognition Using a Flow-Based Latent-Space Representation". En 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.
Texto completoPeychev, Momchil, Anian Ruoss, Mislav Balunović, Maximilian Baader y Martin Vechev. "Latent Space Smoothing for Individually Fair Representations". En Lecture Notes in Computer Science, 535–54. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19778-9_31.
Texto completoPolderman, Jan Willem y Jan C. Willems. "Elimination of Latent Variables and State Space Representations". En 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.
Texto completoWang, Zhendong, Isak Samsten, Rami Mochaourab y Panagiotis Papapetrou. "Learning Time Series Counterfactuals via Latent Space Representations". En Discovery Science, 369–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88942-5_29.
Texto completoSchlemper, Jo, Ozan Oktay, Wenjia Bai, Daniel C. Castro, Jinming Duan, Chen Qin, Jo V. Hajnal y Daniel Rueckert. "Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning". En 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.
Texto completoLópez Diez, Paula, Jan Margeta, Khassan Diab, François Patou y Rasmus R. Paulsen. "Unsupervised Classification of Congenital Inner Ear Malformations Using DeepDiffusion for Latent Space Representation". En Lecture Notes in Computer Science, 652–62. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43904-9_63.
Texto completoBetancourt, Roland. "Extended in the Imagination: The Representation of Architectural Space in Byzantium". En 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.
Texto completoActas de conferencias sobre el tema "Representation space / Latent space"
Liu, Zitu, Jiawang Li, Yue Liu, Qun Liu, Guoyin Wang y Yike Guo. "Interpretability Latent Space Method: Exploiting Shapley Representation to Explain Latent Space". En 2021 7th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2021. http://dx.doi.org/10.1109/bigdia53151.2021.9619687.
Texto completoAttia, Mohamed, MennattAllah H. Attia, Julie Iskander, Khaled Saleh, Darius Nahavandi, Ahmed Abobakr, Mohammed Hossny y Saeid Nahavandi. "Fingerprint Synthesis Via Latent Space Representation". En 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914499.
Texto completoThuruthel, Thomas George, Kieran Gilday y Fumiya Iida. "Drift-Free Latent Space Representation for Soft Strain Sensors". En 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft). IEEE, 2020. http://dx.doi.org/10.1109/robosoft48309.2020.9116021.
Texto completoXu, Yi Tian, Yaqiao Li y David Meger. "Human Motion Prediction Via Pattern Completion in Latent Representation Space". En 2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019. http://dx.doi.org/10.1109/crv.2019.00016.
Texto completoDu, Sihua, Xiaoming Liu y Guan Yang. "Latent space knowledge representation enhancement for low resource machine translation". En International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), editado por Lin Wang y Xiaogang Liu. SPIE, 2023. http://dx.doi.org/10.1117/12.2679623.
Texto completoDos Santos, Anderson Carlos y Valdir Grassi. "Pedestrian Trajectory Prediction with Pose Representation and Latent Space Variables". En 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.
Texto completoNerurkar, Pranav, Madhav Chandane y Sunil Bhirud. "Representation learning for social networks using Homophily based Latent Space Model". En COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3312614.3312627.
Texto completoBielawski, Romain y Rufin VanRullen. "CLIP-based image captioning via unsupervised cycle-consistency in the latent space". En 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.
Texto completoEgiazarian, Vage, Savva Ignatyev, Alexey Artemov, Oleg Voynov, Andrey Kravchenko, Youyi Zheng, Luiz Velho y Evgeny Burnaev. "Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds". En 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009102604210428.
Texto completoSong, Dan, Carl Henrik Ek, Kai Huebner y Danica Kragic. "Embodiment-specific representation of robot grasping using graphical models and latent-space discretization". En 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048145.
Texto completoInformes sobre el tema "Representation space / Latent space"
Saiegh, Sebastián. Partisanship, Ideology, and Representation in Latin America. Inter-American Development Bank, agosto de 2014. http://dx.doi.org/10.18235/0011656.
Texto completoHoff, Peter D., Adrian E. Raftery y Mark S. Handcock. Latent Space Approaches to Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2001. http://dx.doi.org/10.21236/ada458734.
Texto completoZhytaryuk, Maryan y Iryna Ivanova. ANTI-RUSSIAN NARRATIVES OF YURIY SHVETS (ON THE MATERIALS OF HIS AUTHOR’S YOUTUBE CHANNEL). Ivan Franko National University of Lviv, marzo de 2024. http://dx.doi.org/10.30970/vjo.2024.54-55.12154.
Texto completoXuping, Xie, Tang Qi y Tang Xianzhu. Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models. Office of Scientific and Technical Information (OSTI), junio de 2024. http://dx.doi.org/10.2172/2377685.
Texto completoLubold, Shane, Arun Chandrasekhar y Tyler McCormick. Identifying the Latent Space Geometry of Network Models through Analysis of Curvature. Cambridge, MA: National Bureau of Economic Research, diciembre de 2020. http://dx.doi.org/10.3386/w28273.
Texto completoAganj, Iman, Christophe Lenglet y Guillermo Sapiro. ODF Maxima Extraction in Spherical Harmonic Representation via Analytical Search Space Reduction. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2010. http://dx.doi.org/10.21236/ada540656.
Texto completoSolomon, A. D., M. D. Morris, J. Martin y 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), abril de 1986. http://dx.doi.org/10.2172/5777340.
Texto completoAggio, Carlos. 'Lady Leaders': The Case of Quotas for Women's Representation in Argentina. Inter-American Development Bank, julio de 2002. http://dx.doi.org/10.18235/0006873.
Texto completoChervinchuk, Alina. THE CONCEPT OF ENEMY: REPRESENTATION IN THE UKRAINIAN MILITARY DOCUMENTARIES. Ivan Franko National University of Lviv, febrero de 2021. http://dx.doi.org/10.30970/vjo.2021.49.11063.
Texto completoKularatne, Dhanushka N., Subhrajit Bhattacharya y 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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