Готові списки джерел за темами / Dynamic Representation Learning

Добірка наукової літератури з теми "Dynamic Representation Learning"

Автор: Grafiati

Опубліковано: 22 червня 2024

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Dynamic Representation Learning".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "Dynamic Representation Learning":

Lee, Jungmin, and Wongyoung Lee. "Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration." Korean Society of Culture and Convergence 44, no. 9 (September 30, 2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This study is an attempt to propose a multiple representational metaphor education model that combines linguistic representation and visual representation using online telestration. The advent of the media and online era has incorporated not only the understanding of linguistic representation l but also the understanding of visual representation into an important phase of cognitive behavior and requires the implementation of online learning. In such an era's needs, it can be said that teaching-learning makes metaphors be used as a tool for thinking and cognition in an online environment, learning leads learners to a new horizon of perception by combining linguistic representation and visual representation. The multiple representational metaphor education model using online telestration will have a two-way dynamic interaction in an online environment, and it will be possible to improve learning capabilities by expressing various representations. Multiple representational metaphor education using online telestration will allow us to consider new perspectives and various possibilities of expression to interpret the world by converging and rephrasing verbal and visual representations using media in an online environment.

Biswal, Siddharth, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits, and Jimeng Sun. "Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 557–64. http://dx.doi.org/10.1609/aaai.v34i01.5394.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on clinical trial recruitment, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose Doctor2Vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, Doctor2Vec utilizes a dynamic memory network where the doctor's experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2,609 trials, 25K doctors and 430K patients, Doctor2Vec demonstrated improved performance over the best baseline by up to 8.7% in PR-AUC. We also demonstrated that the Doctor2Vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with 13.7% improvement or for rare diseases with 8.1% improvement in PR-AUC.

Wang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei, and Yanli Shao. "Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis." Electronics 12, no. 13 (July 7, 2023): 2986. http://dx.doi.org/10.3390/electronics12132986.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years. This paper focuses on the representation learning of multimodal data to reach higher prediction results. We propose a model to assist in learning modality representations with multitask learning and contrastive learning. In addition, our approach obtains dynamic weights by considering the homoscedastic uncertainty of each task in multitask learning. Specially, we design two groups of subtasks, which predict the sentiment polarity of unimodal and bimodal representations, to assist in learning representation through a hard parameter-sharing mechanism in the upstream neural network. A loss weight is learned according to the homoscedastic uncertainty of each task. Moreover, a training strategy based on contrastive learning is designed to balance the inconsistency between training and inference caused by the randomness of the dropout layer. This method minimizes the MSE between two submodels. Experimental results on the MOSI and MOSEI datasets show our method achieves better performance than the current state-of-the-art methods by comprehensively considering the intramodality and intermodality interaction information.

Goyal, Palash, Sujit Rokka Chhetri, and Arquimedes Canedo. "dyngraph2vec: Capturing network dynamics using dynamic graph representation learning." Knowledge-Based Systems 187 (January 2020): 104816. http://dx.doi.org/10.1016/j.knosys.2019.06.024.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Han, Liangzhe, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, and Tongyu Zhu. "Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical observations for a pre-defined prediction task. Different from the existing research, this paper aims to provide a generic and dynamic representation learning method for crowd flow modeling. The main idea of our method is to maintain a continuous-time representation for each node, and update the representations of all nodes continuously according to the streaming observed data. Along this line, a particular encoder-decoder architecture is proposed, where the encoder converts the newly happened transactions into a timestamped message, and then the representations of related nodes are updated according to the generated message. The role of the decoder is to guide the representation learning process by reconstructing the observed transactions based on the most recent node representations. Moreover, a number of virtual nodes are added to discover macro-level spatial patterns and also share the representations among spatially-interacted stations. Experiments have been conducted on two real-world datasets for four popular prediction tasks in crowd flow modeling. The result demonstrates that our method could achieve better prediction performance for all the tasks than baseline methods.

Jiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao, and Huaming Wu. "Contrastive representation learning on dynamic networks." Neural Networks 174 (June 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Radulescu, Angela, Yeon Soon Shin, and Yael Niv. "Human Representation Learning." Annual Review of Neuroscience 44, no. 1 (July 8, 2021): 253–73. http://dx.doi.org/10.1146/annurev-neuro-092920-120559.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments.

Liu, Dianbo, Alex Lamb, Xu Ji, Pascal Junior Tikeng Notsawo, Michael Mozer, Yoshua Bengio, and Kenji Kawaguchi. "Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically shown that discretization of representations leads to improved generalization, including in reinforcement learning where discretization can be used to bottleneck multi-agent communication to promote agent specialization and robustness. The discretization tightness of most VQ-based methods is defined by the number of discrete codes in the representation vector and the codebook size, which are fixed as hyperparameters. In this work, we propose learning to dynamically select discretization tightness conditioned on inputs, based on the hypothesis that data naturally contains variations in complexity that call for different levels of representational coarseness which is observed in many heterogeneous data sets. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks with heterogeneity in representations.

Deng, Yongjian, Hao Chen, and Youfu Li. "A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (March 24, 2024): 1492–500. http://dx.doi.org/10.1609/aaai.v38i2.27914.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches learning sparse point-based representations through graph CNNs (GCN) become more popular. Yet, these graph techniques hold lower performance than their frame-based counterpart due to two issues: (i) Biased graph structures that don't properly incorporate varied attributes (such as semantics, and spatial and temporal signals) for each vertex, resulting in inaccurate graph representations. (ii) A shortage of robust pretrained models. Here we solve the first problem by proposing a new event-based GCN (EDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To address the second problem, we introduce a novel learning framework called cross-representation distillation (CRD), which leverages the dense representation of events as a cross-representation auxiliary to provide additional supervision and prior knowledge for the event graph. This frame-to-graph distillation allows us to benefit from the large-scale priors provided by CNNs while still retaining the advantages of graph-based models. Extensive experiments show our model and learning framework are effective and generalize well across multiple vision tasks.

Li, Jintang, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, and Changhua Meng. "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from computation and memory overheads on large temporal graphs. So far, scalability of dynamic graph representation learning on large temporal graphs remains one of the major challenges. In this paper, we present a scalable framework, namely SpikeNet, to efficiently capture the temporal and structural patterns of temporal graphs. We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs. As a low-power alternative to RNNs, SNNs explicitly model graph dynamics as spike trains of neuron populations and enable spike-based propagation in an efficient way. Experiments on three large real-world temporal graph datasets demonstrate that SpikeNet outperforms strong baselines on the temporal node classification task with lower computational costs. Particularly, SpikeNet generalizes to a large temporal graph (2.7M nodes and 13.9M edges) with significantly fewer parameters and computation overheads.

Більше джерел

Дисертації з теми "Dynamic Representation Learning":

Stefanidis, Achilleas. "Dynamic Graph Representation Learning on Enterprise Live Video Streaming Events." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278817.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Enterprises use live video streaming as a mean of communication. Streaming high-quality video to thousands of devices in a corporate network is not an easy task; the bandwidth requirements often exceed the network capacity. For that matter, Peer-To-Peer (P2P) networks have been proven beneficial, as peers can exchange content efficiently by utilizing the topology of the corporate network. However, such networks are dynamic and their topology might not always be known. In this project we propose ABD, a new dynamic graph representation learning approach, which aims to estimate the bandwidth capacity between peers in a corporate network. The architecture of ABDis adapted to the properties of corporate networks. The model is composed of an attention mechanism and a decoder. The attention mechanism produces node embeddings, while the decoder converts those embeddings into bandwidth predictions. The model aims to capture both the dynamicity and the structure of the dynamic network, using an advanced training process. The performance of ABD is tested with two dynamic graphs which were produced by real corporate networks. Our results show that ABD achieves better results when compared to existing state-of-the-art dynamic graph representation learning models.
Företag använder live video streaming för både intern och extern kommunikation. Strömmning av hög kvalitet video till tusentals tittare i ett företagsnätverk är inte enkelt eftersom bandbreddskraven ofta överstiger kapaciteten på nätverket. För att minska lasten på nätverket har Peer-to-Peer (P2P) nätverk visat sig vara en lösning. Här anpassar sig P2P nätverket efter företagsnätverkets struktur och kan därigenom utbyta video data på ett effektivt sätt. Anpassning till ett företagsnätverk är ett utmanande problem eftersom dom är dynamiska med förändring över tid och kännedom över topologin är inte alltid tillgänglig. I det här projektet föreslår vi en ny lösning, ABD, en dynamisk approach baserat på inlärning av grafrepresentationer. Vi försöker estimera den bandbreddskapacitet som finns mellan två peers eller tittare. Architekturen av ABD anpassar sig till egenskaperna av företagsnätverket. Själva modellen bakom ABD använder en koncentrationsmekanism och en avkodare. Attention mekanismen producerar node embeddings, medan avkodaren konverterar embeddings till estimeringar av bandbredden. Modellen fångar upp dynamiken och strukturen av nätverket med hjälp av en avancerad träningsprocess. Effektiviteten av ABD är testad på två dynamiska nätverksgrafer baserat på data från riktiga företagsnätverk. Enligt våra experiment har ABD bättre resultat när man jämför med andra state-of the-art modeller för inlärning av dynamisk grafrepresentation.

Liemhetcharat, Somchaya. "Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/304.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Forming an effective multi-robot team to perform a task is a key problem in many domains. The performance of a multi-robot team depends on the robots the team is composed of, where each robot has different capabilities. Team performance has previously been modeled as the sum of single-robot capabilities, and these capabilities are assumed to be known. Is team performance just the sum of single-robot capabilities? This thesis is motivated by instances where agents perform differently depending on their teammates, i.e., there is synergy in the team. For example, in human sports teams, a well-trained team performs better than an allstars team composed of top players from around the world. This thesis introduces a novel model of team synergy — the Synergy Graph model — where the performance of a team depends on each robot’s individual capabilities and a task-based relationship among them. Robots are capable of learning to collaborate and improving team performance over time, and this thesis explores how such robots are represented in the Synergy Graph Model. This thesis contributes a novel algorithm that allocates training instances for the robots to improve, so as to form an effective multi-robot team. The goal of team formation is the optimal selection of a subset of robots to perform the task, and this thesis contributes team formation algorithms that use a Synergy Graph to form an effective multi-robot team with high performance. In particular, the performance of a team is modeled with a Normal distribution to represent the nondeterminism of the robots’ actions in a dynamic world, and this thesis introduces the concept of a δ-optimal team that trades off risk versus reward. Further, robots may fail from time to time, and this thesis considers the formation of a robust multi-robot team that attains high performance even if failures occur. This thesis considers ad hoc teams, where the robots of the team have not collaborated together, and so their capabilities and synergy are initially unknown. This thesis contributes a novel learning algorithm that uses observations of team performance to learn a Synergy Graph that models the capabilities and synergy of the team. Further, new robots may become available, and this thesis introduces an algorithm that iteratively updates a Synergy Graph with new robots.

McGarity, Michael Computer Science &amp Engineering Faculty of Engineering UNSW. "Heterogeneous representations for reinforcement learning control of dynamic systems." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2004. http://handle.unsw.edu.au/1959.4/19350.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Intelligent agents are designed to interact with, and learn about, their environment so that they can act purposefully towards a goal. One class of problems encountered in building such agents is learning how to respond to dynamic systems with a continuous state space. The goals of this dissertation are to develop a framework for understanding the behaviour of partitioned dynamic systems with continuous underlying state and to translate this framework into algorithms which adaptively form a partition of the continuous space such that the partitioned system is more easily learned and controlled, and such that the control law may be easily explained in intuitive ways. Currently, algorithms which learn a control policy for partitioned continuous state space systems treat the partitioned system as an approximation to a Markov chain. I give conditions for the partitioned system to be a Markov chain, a semi-Markov process and a new class of system, a weak-semi-Markov process. The weak-semi-Markov model is shown to model partitioned dynamic systems with greater economy than other surveyed models. The behaviour of a partitioned state space system in the area around the region boundaries is also considered. I use the theory of sliding surfaces, and some heuristic arguments to recommend region boundary shape and position. The concept of 'staying on the boundary' then becomes a robust and relatively easy subgoal within the control algorithm. The concept of 'reaching the sliding surface' as a subgoal is used as the basis for an intuitive explanation of the learnt controller. I present an algorithm based on this concept which explains the behaviour of a learnt controller in ways not previously available to a machine learning algorithms. Finally, the Markov Property and the theory of Sliding Mode Control are used as the basis of a class of recursive algorithms. These algorithms adaptively find a partition, and simultaneously use this partition in conjunction with one of five reinforcement learning algorithms to find a control policy based on that partition. This technique is shown to work very well in learning, controlling and explaining a variety of physical systems, from a monorail to a container crane.

Woodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of reconstructing proper networks, and this objective will be addressed in three parts. The first part lays the foundation for the theory of mathematical representations of proper networks, including an exposition on when such networks are well-posed (i.e., physically realizable). The second part studies the notions of abstractions of a network, which are other networks that preserve certain properties of the original network but contain less structural information. As such, abstractions require less a priori information to reconstruct from data than the original network, which allows previously-unsolvable problems to become solvable. The third part addresses our original objective and presents reconstruction algorithms to recover proper networks in both the time domain and in the frequency domain.

Ribeiro, Andre Figueiredo. "Graph dynamics : learning and representation." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34184.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.
Includes bibliographical references (p. 58-60).
Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a framework for representation, graphs invite us to simplify and view the world as objects of pure structure whose properties are fixed in time, while the phenomena they are supposed to model are actually often changing. A node alone cannot represent a table leg, for example, because a table leg is not one structure (it can have many different shapes, colors, or it can be seen in many different settings, lighting conditions, etc.) Theories of knowledge representation have in general concentrated on the stability of symbols - on the fact that people often use properties that remain unchanged across different contexts to represent an object (in vision, these properties are called invariants). However, on closer inspection, objects are variable as well as stable. How are we to understand such problems? How is that assembling a large collection of changing components into a system results in something that is an altogether stable collection of parts?
(cont.) The work here presents one approach that we came to encompass by the phrase "graph dynamics". Roughly speaking, dynamical systems are systems with states that evolve over time according to some lawful "motion". In graph dynamics, states are graphical structures, corresponding to different hypothesis for representation, and motion is the correction or repair of an antecedent structure. The adapted structure is an end product on a path of test and repair. In this way, a graph is not an exact record of the environment but a malleable construct that is gradually tightened to fit the form it is to reproduce. In particular, we explore the concept of attractors for the graph dynamical system. In dynamical systems theory, attractor states are states into which the system settles with the passage of time, and in graph dynamics they correspond to graphical states with many repairs (states that can cope with many different contingencies). In parallel with introducing the basic mathematical framework for graph dynamics, we define a game for its control, its attractor states and a method to find the attractors. From these insights, we work out two new algorithms, one for Bayesian network discovery and one for active learning, which in combination we use to undertake the object recognition problem in computer vision. To conclude, we report competitive results in standard and custom-made object recognition datasets.
by Andre Figueiredo Ribeiro.
S.M.

Terreau, Enzo. "Apprentissage de représentations d'auteurs et d'autrices à partir de modèles de langue pour l'analyse des dynamiques d'écriture." Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20001.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

La démocratisation récente et massive des outils numériques a donné à tous le moyen de produire de l'information et de la partager sur le web, que ce soit à travers des blogs, des réseaux sociaux, des plateformes de partage, ... La croissance exponentielle de cette masse d'information disponible, en grande partie textuelle, nécessite le développement de modèles de traitement automatique du langage naturel (TAL), afin de la représenter mathématiquement pour ensuite la classer, la trier ou la recommander. C'est l'apprentissage de représentation. Il vise à construire un espace de faible dimension où les distances entre les objets projetées (mots, textes) reflètent les distances constatées dans le monde réel, qu'elles soient sémantique, stylistique, ...La multiplication des données disponibles, combinée à l'explosion des moyens de calculs et l'essor de l'apprentissage profond à permis de créer des modèles de langue extrêmement performant pour le plongement des mots et des documents. Ils assimilent des notions sémantiques et de langue complexes, en restant accessibles à tous et facilement spécialisables sur des tâches ou des corpus plus spécifiques. Il est possible de les utiliser pour construire des plongements d'auteurices. Seulement il est difficile de savoir sur quels aspects un modèle va se focaliser pour les rapprocher ou les éloigner. Dans un cadre littéraire, il serait préférable que les similarités se rapportent principalement au style écrit. Plusieurs problèmes se posent alors. La définition du style littéraire est floue, il est difficile d'évaluer l'écart stylistique entre deux textes et donc entre leurs plongements. En linguistique computationnelle, les approches visant à le caractériser sont principalement statistiques, s'appuyant sur des marqueurs du langage. Fort de ces constats, notre première contribution propose une méthode d'évaluation de la capacité des modèles de langue à appréhender le style écrit. Nous aurons au préalable détaillé comment le texte est représenté en apprentissage automatique puis en apprentissage profond, au niveau du mot, du document puis des auteurices. Nous aurons aussi présenté le traitement de la notion de style littéraire en TAL, base de notre méthode. Le transfert de connaissances entre les boîtes noires que sont les grands modèles de langue et ces méthodes issues de la linguistique n'en demeure pas moins complexe. Notre seconde contribution vise à réconcilier ces approches via un modèle d'apprentissage de représentations d'auteurices se focalisant sur le style, VADES (Variational Author and Document Embedding with Style). Nous nous comparons aux méthodes existantes et analysons leurs limites dans cette optique-là. Enfin, nous nous intéressons à l'apprentissage de plongements dynamiques d'auteurices et de documents. En effet, l'information temporelle est cruciale et permet une représentation plus fine des dynamiques d'écriture. Après une présentation de l'état de l'art, nous détaillons notre dernière contribution, B²ADE (Brownian Bridge for Author and Document Embedding), modélisant les auteurices comme des trajectoires. Nous finissons en décrivant plusieurs axes d'améliorations de nos méthodes ainsi que quelques problématiques pour de futurs travaux
The recent and massive democratization of digital tools has empowered individuals to generate and share information on the web through various means such as blogs, social networks, sharing platforms, and more. The exponential growth of available information, mostly textual data, requires the development of Natural Language Processing (NLP) models to mathematically represent it and subsequently classify, sort, or recommend it. This is the essence of representation learning. It aims to construct a low-dimensional space where the distances between projected objects (words, texts) reflect real-world distances, whether semantic, stylistic, and so on.The proliferation of available data, coupled with the rise in computing power and deep learning, has led to the creation of highly effective language models for word and document embeddings. These models incorporate complex semantic and linguistic concepts while remaining accessible to everyone and easily adaptable to specific tasks or corpora. One can use them to create author embeddings. However, it is challenging to determine the aspects on which a model will focus to bring authors closer or move them apart. In a literary context, it is preferable for similarities to primarily relate to writing style, which raises several issues. The definition of literary style is vague, assessing the stylistic difference between two texts and their embeddings is complex. In computational linguistics, approaches aiming to characterize it are mainly statistical, relying on language markers. In light of this, our first contribution is a framework to evaluate the ability of language models to grasp writing style. We will have previously elaborated on text embedding models in machine learning and deep learning, at the word, document, and author levels. We will also have presented the treatment of the notion of literary style in Natural Language Processing, which forms the basis of our method. Transferring knowledge between black-box large language models and these methods derived from linguistics remains a complex task. Our second contribution aims to reconcile these approaches through a representation learning model focusing on style, VADES (Variational Author and Document Embedding with Style). We compare our model to state-of-the-art ones and analyze their limitations in this context.Finally, we delve into dynamic author and document embeddings. Temporal information is crucial, allowing for a more fine-grained representation of writing dynamics. After presenting the state of the art, we elaborate on our last contribution, B²ADE (Brownian Bridge Author and Document Embedding), which models authors as trajectories. We conclude by outlining several leads for improving our methods and highlighting potential research directions for the future

Pinder, Ross Andrew. "Representative learning design in dynamic interceptive actions." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59803/1/Ross_Pinder_Thesis.pdf.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The overarching aim of this thesis was to investigate how processes of perception and action emerge under changing informational constraints during performance of multi-articular interceptive actions. Interceptive actions provide unique opportunities to study processes of perception and action in dynamic performance environments. The movement model used to exemplify the functionally coupled relationship between perception and action, from an ecological dynamics perspective, was cricket batting. Ecological dynamics conceptualises the human body as a complex system composed of many interacting sub-systems, and perceptual and motor system degrees of freedom, which leads to the emergence of patterns of behaviour under changing task constraints during performance. The series of studies reported in the Chapters of this doctoral thesis contributed to understanding of human behaviour by providing evidence of key properties of complex systems in human movement systems including self-organisation under constraints and meta-stability. Specifically, the studies: i) demonstrated how movement organisation (action) and visual strategies (perception) of dynamic human behaviour are constrained by changing ecological (especially informational) task constraints; (ii) provided evidence for the importance of representative design in experiments on perception and action; and iii), provided a principled theoretical framework to guide learning design in acquisition of skill in interceptive actions like cricket batting.

Moremoholo, T. P. "A review of how to optimize learning from external representations." Journal for New Generation Sciences, Vol 11, Issue 2: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/635.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Published Article
This article reviews research on learning with external representations and provides a theoretical background on how to optimize learning from external representations. General factors, such as the type of material to be learned, learner characteristics and the testing method, are some of the variables that can determine if graphic medium can increase a subject's comprehension and if such comprehension can be accurately measured. These factors are discussed and represented by a model to suggest how external representations can be effectively used in a learning environment. Two key conclusions are drawn from the observation made in these studies. Firstly, the proper design of a particular external representation and supporting text can promote relevant activities that ultimately contribute to fuller understanding of the content. Secondly, external representations must be developed to address the size complexity and variety of the content that must be analysed in order to extract knowledge for scientific discovery.

Delasalles, Edouard. "Inferring and Predicting Dynamic Representations for Structured Temporal Data." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS296.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Les données temporelles constituent une partie importante des données digitales. Prévoir leurs prochaines valeurs est une tâche importante et difficile. Les méthodes statistiques standard sont fondées sur des modèles linéaires souvent limitées aux données de faible dimension. Ici, nous utilisons plutôt des méthodes d'apprentissage profond capables de traiter des données structurées en haute dimension. Dans cette thèse, nous nous intéressons aux modèles à variables latentes. Contrairement aux modèles auto-régressifs qui utilisent directement des données passées pour effectuer des prédictions, les modèles latents infèrent des représentations vectorielles qui sont ensuite prédites. Nous proposons d'abord un modèle latent structuré pour la prévision de données spatio-temporelles. Des variables latentes sont inférés à partir d'un ensemble de points dans l'espace où des données sont collectées (météo, trafic). Ensuite, la structure spatiale est utilisée dans la fonction dynamique. Le modèle est également capable de découvrir des corrélations entre des séries sans information spatiale préalable. Ensuite, nous nous intéressons à la prédiction des distributions de données. Nous proposons un modèle qui génère des variables latentes utilisées pour conditionner un modèle génératif. Les données textuelles sont utilisées pour évaluer le modèle sur la modélisation diachronique du langage. Enfin, nous proposons un modèle de prédiction stochastique. Il utilise les premières valeurs des séquences pour générer plusieurs futurs possibles. Ici, le modèle génératif n'est pas conditionné à une époque absolue, mais à une séquence. Le modèle est appliqué à la prédiction vidéo stochastique
Temporal data constitute a large part of data collected digitally. Predicting their next values is an important and challenging task in domains such as climatology, optimal control, or natural language processing. Standard statistical methods are based on linear models and are often limited to low dimensional data. We instead use deep learning methods capable of handling high dimensional structured data and leverage large quantities of examples. In this thesis, we are interested in latent variable models. Contrary to autoregressive models that directly use past data to perform prediction, latent models infer low dimensional vectorial representations of data on which prediction is performed. Latent vectorial spaces allow us to learn dynamic models that are able to generate high-dimensional and structured data. First, we propose a structured latent model for spatio-temporal data forecasting. Given a set of spatial locations where data such as weather or traffic are collected, we infer latent variables for each location and use spatial structure in the dynamic function. The model is also able to discover correlations between series without prior spatial information. Next, we focus on predicting data distributions, rather than point estimates. We propose a model that generates latent variables used to condition a generative model. Text data are used to evaluate the model on diachronic language modeling. Finally, we propose a stochastic prediction model. It uses the first values of sequences to generate several possible futures. Here, the generative model is not conditioned to an absolute epoch, but to a sequence. The model is applied to stochastic video prediction

Mccosker, Christopher Mark. "Interacting constraints in high performance long jumping." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204284/1/Christopher_McCosker_Thesis.pdf.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This thesis details a series of studies investigating long jump locomotor pointing tasks through a theoretical framework of ecological dynamics. A multi-methods approach was used to investigate athlete behaviours in competition environments revealing the complexities of field-based locomotor pointing tasks, providing important information for the design of practice environments for practitioners. The development of a new substantive theoretical model of perform, respond and manage, presents as a novel tool for practitioners in understanding emergent behaviour in long jump and can be used to better frame representative learning designs. Importantly, athletes need to prepare for more than just a technical performance.

Більше джерел

Книги з теми "Dynamic Representation Learning":

Baum, Susan, and Robin Schader. Using a Positive Lens. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190645472.003.0003.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Getting to know students through their positive attributes, along with any obstacles to their success, can lead to academic, social, and behavioral growth. Defining twice exceptional (2e) as those whose high abilities are coupled with learning and/or behavioral challenges, this chapter illustrates 2e using the metaphor of green. This provides a fresh representation what being 2e means and also highlights why conventional educational plans may not be as effective as desired. To engage 2e students, the chapter introduces a practical, dynamic process––The Suite of Tools™—which allows educators to collect information about students’ strengths, talents, and interests and synthesize the information into a strength-based framework. Such information results in the development of personalized options to leverage strengths and interests for skill development, integrate strengths into the curriculum, and develop a personalized menu of talent development opportunities.

Clavio, Galen. Social Media and Sports. Human Kinetics, 2017. http://dx.doi.org/10.5040/9781718221000.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Social media communications play a huge role in the day-to-day operations of sport teams and organizations. Both current and aspiring sport business professionals need to know how to best leverage social media to meet their organizational goals, and Social Media and Sports With HKPropel Access will help pave the way by emphasizing the strategic, creative, and logistical elements of effective social media practices. Beginning with foundational concepts, students will first examine the history of social media and its impact on sports. They will learn about the categories of content used, including written content, images, produced video, live video, audio, graphics, dynamic visuals, and responses. They will then gain a better understanding of the social media environment by learning how to think about audiences and networks, evaluating how online communities act and interact, and considering key issues that may be encountered. The final chapters of the text assemble the building blocks from previous chapters into practical application, covering brand management strategies and overall social media presence from the perspective of a member of the sports media, a representative of a team or league, or an individual athlete. Related online learning aids, delivered via HKPropel and reviewed annually to stay current with evolving trends, provides a detailed look into major social networks and their technological elements, plus best practices, tips, and tricks for utilizing a variety of social media platforms. It also examines content methodologies, including podcasting, live video, and prerecorded video, and it discusses the use of social management software. Markers throughout the text refer students to the web resource when additional related content is available. Learning aids for students include Professional Insights, sidebars containing interviews with industry insiders; these real-world examples and professional advice provide depth and context to each chapter's content. Key Points highlight important points, end-of-chapter review questions promote practical application and ensure content comprehension, and bolded key terms are defined in an easy-to-reference glossary. Social Media and Sports offers a practical approach to understanding social media communications in the sports industry, with application extending to those working in journalism, public relations, broadcasting, advertising, and other sport business careers where knowledge of effective social media usage will maximize career potential.

Faflik, David. Urban Formalism. Fordham University Press, 2020. http://dx.doi.org/10.5422/fordham/9780823288045.001.0001.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Urban Formalism radically reimagines what it meant to “read” a brave new urban world during the transformative middle decades of the nineteenth century. At a time when contemporaries in the twin capitals of modernity in the West, New York and Paris, were learning to make sense of unfamiliar surroundings, city peoples increasingly looked to the experiential patterns, or forms, from their everyday lives in an attempt to translate urban experience into something they could more easily comprehend. Urban Formalism interrogates both the risks and rewards of an interpretive practice that depended on the mutual relation between urbanism and formalism, at a moment when the subjective experience of the city had reached unprecedented levels of complexity. What did it mean to read a city sidewalk as if it were a literary form, like a poem? On what basis might the material form of a burning block of buildings be received as a pleasurable spectacle? How closely aligned were the ideology and choreography of the political form of a revolutionary street protest? And what were the implications of conceiving of the city’s exciting dynamism in the static visual form of a photographic composition? These are the questions that Urban Formalism asks and begins to answer, with the aim of proposing a revisionist semantics of the city. This book not only provides an original cultural history of forms. It posits a new form of urban history, comprised of the representative rituals of interpretation that have helped give meaningful shape to metropolitan life.

The Expected Knowledge: What can we know about anything and everything? Tiruchirappalli: Sivashanmugam Palaniappan, 2012.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Частини книг з теми "Dynamic Representation Learning":

Feng, Hao, Yan Liu, Ziqiao Zhou, and Jing Chen. "Dynamic Network Change Detection via Dynamic Network Representation Learning." In Communications and Networking, 642–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41114-5_48.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Yu, Yanwei, Huaxiu Yao, Hongjian Wang, Xianfeng Tang, and Zhenhui Li. "Representation Learning for Large-Scale Dynamic Networks." In Database Systems for Advanced Applications, 526–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91458-9_32.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Mendes, M. E. S., and L. Sacks. "Dynamic Knowledge Representation for e-Learning Applications." In Enhancing the Power of the Internet, 259–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45218-8_12.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Fathy, Ahmed, and Kan Li. "TemporalGAT: Attention-Based Dynamic Graph Representation Learning." In Advances in Knowledge Discovery and Data Mining, 413–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_32.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Zhang, Si, Yinglong Xia, Yan Zhu, and Hanghang Tong. "Representation Learning on Dynamic Network of Networks." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 298–306. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch34.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Lowe, Richard. "User-Controllable Animated Diagrams: The Solution for Learning Dynamic Content?" In Diagrammatic Representation and Inference, 355–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25931-2_38.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Xu, Liancheng, Xiaoxiang Wang, Lei Guo, Jinyu Zhang, Xiaoqi Wu, and Xinhua Wang. "Candidate-Aware Dynamic Representation for News Recommendation." In Artificial Neural Networks and Machine Learning – ICANN 2023, 272–84. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44195-0_23.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Li, Yongfang, Liang Chang, Guanjun Rao, Phatpicha Yochum, Yiqin Luo, and Tianlong Gu. "Representation Learning for Knowledge Graph with Dynamic Step." In Communications in Computer and Information Science, 382–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2203-7_29.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Denisov, Mikhail, Anton Anikin, and Oleg Sychev. "Dynamic Flowcharts for Enhancing Learners’ Understanding of the Control Flow During Programming Learning." In Diagrammatic Representation and Inference, 408–11. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_42.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Gong, Changwei, Changhong Jing, Yanyan Shen, and Shuqiang Wang. "Dynamic Community Detection via Adversarial Temporal Graph Representation Learning." In Neural Computing for Advanced Applications, 1–13. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6135-9_1.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Dynamic Representation Learning":

Kose, Oyku Deniz, and Yanning Shen. "Dynamic Fair Node Representation Learning." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10094834.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Zhang, Siwei, Yun Xiong, Yao Zhang, Yiheng Sun, Xi Chen, Yizhu Jiao, and Yangyong Zhu. "RDGSL: Dynamic Graph Representation Learning with Structure Learning." In CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583780.3615023.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Tong, Minglei, and Han Hong. "Learning sparse representation for dynamic gesture recogniton." In 2015 IEEE Workshop on Signal Processing Systems (SiPS). IEEE, 2015. http://dx.doi.org/10.1109/sips.2015.7345021.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Tan, Zhen, Xiang Zhao, Yang Fang, Weidong Xiao, and Jiuyang Tang. "Knowledge Representation Learning via Dynamic Relation Spaces." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0102.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Racz, Janos, and Tamas Klotz. "Knowledge representation by dynamic competitive learning techniques." In SPIE Proceedings, edited by Steven K. Rogers. SPIE, 1991. http://dx.doi.org/10.1117/12.45015.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Tian, Sheng, Ruofan Wu, Leilei Shi, Liang Zhu, and Tao Xiong. "Self-supervised Representation Learning on Dynamic Graphs." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3482389.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Wu, Zian, Huijun Tang, and Huan Liu. "Bayesian Contrastive Representation Learning for Dynamic Graph." In 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). IEEE, 2022. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00355.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Luo, Yiqin, Liang Chang, Guanjun Rao, Wei Chen, and Tianlong Gu. "Representation Learning for Knowledge Graph with Dynamic Margin." In 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.10171.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Liu, Yang, Yan Liu, Shenghua Zhong, and Keith C. C. Chan. "Trajectory representation of dynamic texture via manifold learning." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5496195.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Liu, Zhijun, Chao Huang, Yanwei Yu, Peng Song, Baode Fan, and Junyu Dong. "Dynamic Representation Learning for Large-Scale Attributed Networks." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3411945.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "Dynamic Representation Learning":

Mishra, Umakant, and Sagar Gautam. Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1891184.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).