Relevant bibliographies by topics / Robust Representations

Contents

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

Academic literature on the topic 'Robust Representations'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Robust Representations"

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Yang, Shuo, Tianyu Guo, Yunhe Wang, and Chang Xu. "Adversarial Robustness through Disentangled Representations." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3145–53. http://dx.doi.org/10.1609/aaai.v35i4.16424.

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Despite the remarkable empirical performance of deep learning models, their vulnerability to adversarial examples has been revealed in many studies. They are prone to make a susceptible prediction to the input with imperceptible adversarial perturbation. Although recent works have remarkably improved the model's robustness under the adversarial training strategy, an evident gap between the natural accuracy and adversarial robustness inevitably exists. In order to mitigate this problem, in this paper, we assume that the robust and non-robust representations are two basic ingredients entangled i
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Kuo, Yen-Ling. "Learning Representations for Robust Human-Robot Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22673. http://dx.doi.org/10.1609/aaai.v38i20.30289.

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For robots to robustly and flexibly interact with humans, they need to acquire skills to use across scenarios. One way to enable the generalization of skills is to learn representations that are useful for downstream tasks. Learning a representation for interactions requires an understanding of what (e.g., objects) as well as how (e.g., actions, controls, and manners) to interact with. However, most existing language or visual representations mainly focus on objects. To enable robust human-robot interactions, we need a representation that is not just grounded at the object level but to reason
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Iddianozie, Chidubem, and Gavin McArdle. "Towards Robust Representations of Spatial Networks Using Graph Neural Networks." Applied Sciences 11, no. 15 (2021): 6918. http://dx.doi.org/10.3390/app11156918.

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The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogene
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Chateauneuf, Alain, Xiangyu Qu, Caroline Ventura та Vassili Vergopoulos. "Robust α-maxmin representations". Journal of Mathematical Economics 114 (жовтень 2024): 103045. http://dx.doi.org/10.1016/j.jmateco.2024.103045.

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Vu, Hung, Tu Dinh Nguyen, Trung Le, Wei Luo, and Dinh Phung. "Robust Anomaly Detection in Videos Using Multilevel Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5216–23. http://dx.doi.org/10.1609/aaai.v33i01.33015216.

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Detecting anomalies in surveillance videos has long been an important but unsolved problem. In particular, many existing solutions are overly sensitive to (often ephemeral) visual artifacts in the raw video data, resulting in false positives and fragmented detection regions. To overcome such sensitivity and to capture true anomalies with semantic significance, one natural idea is to seek validation from abstract representations of the videos. This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data. The framework consists
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Ho, Edward Kei Shiu, and Lai Wan Chan. "Analyzing Holistic Parsers: Implications for Robust Parsing and Systematicity." Neural Computation 13, no. 5 (2001): 1137–70. http://dx.doi.org/10.1162/08997660151134361.

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Holistic parsers offer a viable alternative to traditional algorithmic parsers. They have good generalization performance and are robust inherently. In a holistic parser, parsing is achieved by mapping the connectionist representation of the input sentence to the connectionist representation of the target parse tree directly. Little prior knowledge of the underlying parsing mechanism thus needs to be assumed. However, it also makes holistic parsing difficult to understand. In this article, an analysis is presented for studying the operations of the confluent pre-order parser (CPP). In the anal
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Yang, Qing, Jun Chen, and Najla Al-Nabhan. "Data representation using robust nonnegative matrix factorization for edge computing." Mathematical Biosciences and Engineering 19, no. 2 (2021): 2147–78. http://dx.doi.org/10.3934/mbe.2022100.

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<abstract> <p>As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization a
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Parlett, Beresford N., and Inderjit S. Dhillon. "Relatively robust representations of symmetric tridiagonals." Linear Algebra and its Applications 309, no. 1-3 (2000): 121–51. http://dx.doi.org/10.1016/s0024-3795(99)00262-1.

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Medina, Josep R., and Carlos R. Sanchez‐Carratala. "Robust AR Representations of Ocean Spectra." Journal of Engineering Mechanics 117, no. 12 (1991): 2926–30. http://dx.doi.org/10.1061/(asce)0733-9399(1991)117:12(2926).

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Higashi, Masatake, Fuyuki Torihara, Nobuhiro Takeuchi, Toshio Sata, Tsuyoshi Saitoh, and Mamoru Hosaka. "Robust algorithms for face-based representations." Computer-Aided Design 29, no. 2 (1997): 135–46. http://dx.doi.org/10.1016/s0010-4485(96)00042-5.

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Dissertations / Theses on the topic "Robust Representations"

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Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Thesis, Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096/document.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace
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Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Electronic Thesis or Diss., Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace
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Tran, Brandon Vanhuy. "Building and using robust representations in image classification." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127912.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 115-131).<br>One of the major appeals of the deep learning paradigm is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully capturing abstract, meaningful features of the data. For instance, the pervasiveness of adversarial examples--
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Parekh, Sanjeel. "Learning representations for robust audio-visual scene analysis." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT015/document.

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L'objectif de cette thèse est de concevoir des algorithmes qui permettent la détection robuste d’objets et d’événements dans des vidéos en s’appuyant sur une analyse conjointe de données audio et visuelle. Ceci est inspiré par la capacité remarquable des humains à intégrer les caractéristiques auditives et visuelles pour améliorer leur compréhension de scénarios bruités. À cette fin, nous nous appuyons sur deux types d'associations naturelles entre les modalités d'enregistrements audiovisuels (réalisés à l'aide d'un seul microphone et d'une seule caméra), à savoir la corrélation mouvement/audi
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Parekh, Sanjeel. "Learning representations for robust audio-visual scene analysis." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT015.

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L'objectif de cette thèse est de concevoir des algorithmes qui permettent la détection robuste d’objets et d’événements dans des vidéos en s’appuyant sur une analyse conjointe de données audio et visuelle. Ceci est inspiré par la capacité remarquable des humains à intégrer les caractéristiques auditives et visuelles pour améliorer leur compréhension de scénarios bruités. À cette fin, nous nous appuyons sur deux types d'associations naturelles entre les modalités d'enregistrements audiovisuels (réalisés à l'aide d'un seul microphone et d'une seule caméra), à savoir la corrélation mouvement/audi
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Herdtweck, Christian [Verfasser], and Heinrich [Akademischer Betreuer] Bülthoff. "Learning Data-Driven Representations for Robust Monocular Computer Vision Applications / Christian Herdtweck ; Betreuer: Heinrich Bülthoff." Tübingen : Universitätsbibliothek Tübingen, 2014. http://d-nb.info/1162897317/34.

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Xu, Guanglin. "Optimization under uncertainty: conic programming representations, relaxations, and approximations." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5881.

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In practice, the presence of uncertain parameters in optimization problems introduces new challenges in modeling and solvability to operations research. There are three main paradigms proposed for optimization problems under uncertainty. These include stochastic programming, robust optimization, and sensitivity analysis. In this thesis, we examine, improve, and combine the latter two paradigms in several relevant models and applications. In the second chapter, we study a two-stage adjustable robust linear optimization problem in which the right-hand sides are uncertain and belong to a compact,
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Barbano, Carlo Alberto Maria. "Collateral-Free Learning of Deep Representations : From Natural Images to Biomedical Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT038.

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L’apprentissage profond est devenu l'un des outils prédominants pour résoudre une variété de tâches, souvent avec des performances supérieures à celles des méthodes précédentes. Les modèles d'apprentissage profond sont souvent capables d'apprendre des représentations significatives et abstraites des données sous-jacentes. Toutefois, il a été démontré qu'ils pouvaient également apprendre des caractéristiques supplémentaires, qui ne sont pas nécessairement pertinentes ou nécessaires pour la tâche souhaitée. Cela peut poser un certain nombre de problèmes, car ces informations supplémentaires peuv
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Terzi, Matteo. "Learning interpretable representations for classification, anomaly detection, human gesture and action recognition." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3423183.

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The goal of this thesis is to provide algorithms and models for classification, gesture recognition and anomaly detection with a partial focus on human activity. In applications where humans are involved, it is of paramount importance to provide robust and understandable algorithms and models. A way to accomplish this requirement is to use relatively simple and robust approaches, especially when devices are resource-constrained. The second approach, when a large amount of data is present, is to adopt complex algorithms and models and make them robust and interpretable from a human-like point
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山本, 有作, та Yusaku Yamamoto. "密行列固有値解法の最近の発展(I) : Multiple Relatively Robust Representationsアルゴリズム". 日本応用数理学会, 2005. http://hdl.handle.net/2237/10838.

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Books on the topic "Robust Representations"

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Li, Sheng, and Yun Fu. Robust Representation for Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2.

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Burhans, Robert L. Robert L. Burhans. The University, 1987.

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Wallgrün, Jan Oliver. Hierarchical Voronoi graphs: Spatial representation and reasoning for mobile robots. Springer, 2010.

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Mullane, John. Random Finite Sets for Robot Mapping and SLAM: New Concepts in Autonomous Robotic Map Representations. Springer Berlin Heidelberg, 2011.

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Wolter, Diedrich. Spatial representation and reasoning for robot mapping: A shape-based approach. Springer, 2008.

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Heikkonen, Jukka. Subsymbolic representations, self-organizing maps, and object motion learning. Lappeenranta University of Technology, 1994.

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A, Boswell Sharon, and Washington State Oral History Program., eds. Robert F. Goldsworthy: An oral history. Washington State Oral History Program, Office of the Secretary of State, 1999.

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Fernández, Juan A. Multi-Hierarchical Representation of Large-Scale Space: Applications to Mobile Robots. Springer Netherlands, 2001.

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Krämer, Olav. Narrating Thinking. Representations of the Intellect in Robert Musil and Paul Valéry. Walter de Gruyter, 2009. http://dx.doi.org/10.1515/9783110210941.

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Mage, Tristan. Onze ans ce printemps-là, 1981-1992: Correspondances de Tristan Mage avec Messieurs Robert Badinter, Laurent Fabius, Pierre Mauroy et François Mitterrand relatives à la Constitution et aux méfaits de la représentation proportionnelle pour la désignation des députés. T. Mage, 1992.

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Book chapters on the topic "Robust Representations"

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Li, Sheng, and Yun Fu. "Fundamentals of Robust Representations." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2_2.

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Li, Sheng, and Yun Fu. "Robust Representations for Collaborative Filtering." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2_7.

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Li, Sheng, and Yun Fu. "Robust Representations for Response Prediction." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2_8.

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Li, Sheng, and Yun Fu. "Robust Representations for Outlier Detection." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2_9.

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Li, Sheng, and Yun Fu. "Robust Representations for Person Re-identification." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2_10.

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Li, Hengjian, Lianhai Wang, and Zutao Zhang. "Robust Palmprint Recognition Based on Directional Representations." In Intelligent Information Processing VI. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32891-6_46.

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Som, Anirudh, Kowshik Thopalli, Karthikeyan Natesan Ramamurthy, Vinay Venkataraman, Ankita Shukla, and Pavan Turaga. "Perturbation Robust Representations of Topological Persistence Diagrams." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01234-2_38.

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Vemuri, Baba C., Jundong Liu, and José L. Marroquin. "Robust Multimodal Image Registration Using Local Frequency Representations." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45729-1_17.

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Wallraven, Christian, and Heinrich Bülthoff. "Acquiring Robust Representations for Recognition from Image Sequences." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45404-7_29.

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Chen, Xiang, Xin Xie, Zhen Bi, et al. "Disentangled Contrastive Learning for Learning Robust Textual Representations." In Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93049-3_18.

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Conference papers on the topic "Robust Representations"

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Neshatfar, Sepideh, and Salimeh Yasaei Sekeh. "Robust Subgraph Learning by Monitoring Early Training Representations." In 2025 IEEE 11th International Conference on Intelligent Data and Security (IDS). IEEE, 2025. https://doi.org/10.1109/ids66066.2025.00015.

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John, R. R., I. Ackerely, R. Smith, et al. "Can PET FDG Deep Texture Representations Be Made Robust?" In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10654898.

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Paul, Shubham, Sudharsan Senthil, Preethi Seshadri, Nambi Seshadri, and R. David Koilpillai. "Learning Robust Representations for Communications over Interference-Limited Channels." In 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2025. https://doi.org/10.1109/wcnc61545.2025.10978389.

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Paul, Indraneil, Goran Glavaš, and Iryna Gurevych. "IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.802.

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Chen, Yafeng, Siqi Zheng, Hui Wang, et al. "Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890786.

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Chang, Chenxia, Song Gao, Shaowen Yao, and Yunfei Lu. "Metric Learning for Robust High-Level Representations against Adversarial Attacks." In 2024 IEEE International Conference on Software System and Information Processing (ICSSIP). IEEE, 2024. https://doi.org/10.1109/icssip63203.2024.11012506.

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Jung, Haeji, Changdae Oh, Jooeon Kang, et al. "Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer." In Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.mrl-1.16.

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Bafghi, Reza Akbarian, Nidhin Harilal, Claire Monteleoni, and Maziar Raissi. "MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00729.

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Ramtoula, Benjamin, Daniele De Martini, Matthew Gadd, and Paul Newman. "VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611379.

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Sun, Shaofan, Jiahang Zhang, Guo Tang, Chuanmin Jia, and Jiaying Liu. "Learning Discriminative and Robust Representations for UAV-View Skeleton-Based Action Recognition." In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2024. http://dx.doi.org/10.1109/icmew63481.2024.10645407.

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Reports on the topic "Robust Representations"

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Sznaier, Mario. Multiobject Robust Control of Nonlinear Systems via State Dependent Coefficient Representations and Applications. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada419042.

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Barraquand, Jerome, and Jean-Claude Latombe. Robot Motion Planning: A Distributed Representation Approach. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada209890.

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Feeney, Patricia, Matthias Liffers, Estelle Cheng, and Paul Vierkant. Better Together: Complete Metadata as Robust Infrastructure. Crossref, 2022. http://dx.doi.org/10.13003/m3237yt.

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According to the survey we conducted prior to this webinar series dedicated to the APAC community, metadata quality was one of the most voted topics to be covered in the webinars, which is understandable - out of FAIRsFAIR’s 15 assessment metrics for the FAIRness of research objects, 12 are about metadata. Rich and persistent metadata that incorporate identifiers and encode generic and domain-specific information, accessibility and licensing, and links between objects using standardized vocabulary and communication protocols is the cornerstone of a versatile, equitable, and trustworthy scholar
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Bowyer, Kevin. Development of the Aspect Graph Representation for Use in Robot Vision. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada247109.

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Boyer, Marcel. Comments on competition policy and labour markets. CIRANO, 2022. http://dx.doi.org/10.54932/iqio1721.

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Traditionally, labour concerns have not been top-of-mind when considering competition policy, but the current approach to wage-fixing, anti-poaching, and anti-mobility agreements between firms has been one of the main reasons behind recent Parliamentary attention to competition policy and labour markets. Key stakeholders in academic and policy circles have called for more robust enforcement regarding monopsony / oligopsony power in labour markets, when assessing mergers and acquisitions for example, as well as regarding market power in labour representation (unions) and certification as entry
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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46632.

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Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowle
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Terzyan, Aram. The State of Minority Rights in Uzbekistan: A Comparative Analysis of Tajiks, Russians, and Koreans. Eurasia Institutes, 2023. http://dx.doi.org/10.47669/erd-1-2023.

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This paper examines the state of minority rights in Uzbekistan, focusing on three significant ethnic groups: Tajiks, Russians, and Koreans. It explores the historical context of these minorities, the cultural and linguistic challenges they face, socioeconomic issues, and their political representation. Under the authoritarian rule of Islam Karimov, Uzbekistan emphasized a unified Uzbek identity, often marginalizing minority cultures and languages. Despite President Shavkat Mirziyoyev’s reforms aimed at improving human rights, including the establishment of a Human Rights Ombudsman and the Deve
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Desk, Front. Report on the Regional Focal Points Meeting - Africa and Europe. Commonwealth of Learning (COL), 2023. http://dx.doi.org/10.56059/11599/5316.

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The Regional Focal Points Meeting for Africa and Europe was held in Mahe, Seychelles from 20-21 February 2023. The two-day meeting was chaired by Prof. Asha Kanwar, President and CEO, Commonwealth of Learning (COL) and facilitated by Ms Frances Ferreira, Acting Director: Skills, Mr Robert Okinda, Adviser: TVSD, and Professor Jane-Frances Agbu, Adviser: Higher Education. Official representatives from 20 Commonwealth countries in the region participated in the two-day meeting. The list of participants and agenda of the meeting are included as Annex 1 and 2, respectively.
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Balfour, Lindsay, Adrienne Evans, Marcus Maloney, and Sarah Merry. Postdigital Intimacies for Online Safety. Coventry University, 2023. http://dx.doi.org/10.18552/pdc/2023/0001.

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This report offers a multi-sector response to the Online Safety Bill (OSB). The shape and content of the OSB has generated discussion amongst policy specialists, stakeholders and lobbyists in key services and sectors, political advisors and appointed representatives, and academics and researchers – as well as a general public interested in what the OSB will mean for people made vulnerable or at risk of harms online. We report on the discussions that took place in four co-production workshops with representatives from the areas of: intimate digital health tools and services marketed to those wh
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Dessie, Yadeta, Dagim Habteyesus, Yohannes Dibaba Wado, et al. Ethiopia Humanitarian Violence Against Children and Youth Survey, 2024: Data-to-Action workshop report. Population Council Inc., Population Council Kenya, and the African Population and Health Research Center, 2025. https://doi.org/10.31899/sbsr2025.1007.

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The Government of Ethiopia’s Refugees and Returnees Service (RRS) officials, various government ministry delegates, representatives from International and national Non-Governmental Organisations, UN bodies, and various delivery partners, came together for a Data to Action workshop in Addis Ababa from November 19th to 21st, 2024. During this workshop, participants prioritized violence prevention and response activities, directly informed by the HVACS data. This was achieved by translating the Ethiopia HVACS findings into key indicators that were then mapped into actionable next steps by linking
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