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Journal articles on the topic 'Dynamic Representation Learning'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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1

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 (2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.

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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, learn

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Han, Chengkai, Jingyuan Wang, Yongyao Wang, et al. "Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11763–71. https://doi.org/10.1609/aaai.v39i11.33280.

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Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynamic nature of traffic states and trajectories, which is crucial for downstream tasks. To address this gap, we propose TRACK, a novel framework to bridge traffic state and trajectory data for dynamic road network and trajectory representation learning. TRACK leverages graph attentio

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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 (2020): 557–64. http://dx.doi.org/10.1609/aaai.v34i01.5394.

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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 docto

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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 (2023): 2986. http://dx.doi.org/10.3390/electronics12132986.

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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 representati

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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.

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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 (2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.

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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 t

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Jiao, Pengfei, Hongjiang Chen, Huijun Tang, et al. "Contrastive representation learning on dynamic networks." Neural Networks 174 (June 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.

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

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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

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Liu, Dianbo, Alex Lamb, Xu Ji, et al. "Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.

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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

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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 (2024): 1492–500. http://dx.doi.org/10.1609/aaai.v38i2.27914.

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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 (E

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Li, Jintang, Zhouxin Yu, Zulun Zhu, et al. "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.

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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 pa

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Wu, Wei, and Xuemeng Zhai. "DyLFG: A Dynamic Network Learning Framework Based on Geometry." Entropy 25, no. 12 (2023): 1611. http://dx.doi.org/10.3390/e25121611.

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Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant n

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Wei, Hao, Guyu Hu, Wei Bai, Shiming Xia, and Zhisong Pan. "Lifelong representation learning in dynamic attributed networks." Neurocomputing 358 (September 2019): 1–9. http://dx.doi.org/10.1016/j.neucom.2019.05.038.

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Lee, Dongha, Xiaoqian Jiang, and Hwanjo Yu. "Harmonized representation learning on dynamic EHR graphs." Journal of Biomedical Informatics 106 (June 2020): 103426. http://dx.doi.org/10.1016/j.jbi.2020.103426.

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Zheng, Tingting, Kui Jiang, Hongxun Yao, Yi Xiao, and Zhongyuan Wang. "OODML: Whole Slide Image Classification Meets Online Pseudo-Supervision and Dynamic Mutual Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10626–34. https://doi.org/10.1609/aaai.v39i10.33154.

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Bag-label-based multi-instance learning (MIL) has demonstrated significant performance in whole slide image (WSI) analysis, particularly in pseudo-label-based learning schemes. However, due to inaccurate feature representation and interference, existing MIL methods often yield unreliable pseudo-labels, which spawn undesired predictions. To address these issues, we propose an Online Pseudo-Supervision and Dynamic Mutual Learning (OODML) framework that enhances pseudo-label generation and feature representation while exploring their mutual learning to improve bag-level prediction. Specifically,

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Huang, Yicong, and Zhuliang Yu. "Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models." Entropy 24, no. 2 (2022): 152. http://dx.doi.org/10.3390/e24020152.

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Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent dynamics. On the other hand, emerging evidence suggests that dynamic functional connectivities (DFC) may be responsible for neural activity patterns underlying cognition or behavior. We are interested in studying how DFC are associated with the low-dimensional structure of neural acti

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Scavinner-Dorval, Claire, Rodolphe Bailly, Bhushan Borotikar, Sylvain Brochard, Douraied Ben Salem, and François Rousseau. "Learning disentangled representations for unpaired synthesis of high-resolution dynamic MRI." Machine Learning for Biomedical Imaging 3, February 2025 (2025): 16–37. https://doi.org/10.59275/j.melba.2025-e8dc.

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Image-to-image (I2I) synthesis aims at learning the mapping between two visual domains. Due to the scarcity of paired datasets, learning such transformation may be challenging. With a growing number of publications each year, unpaired I2I synthesis has drawn attention from the research community and has found a great application field in medical imaging. Disentangled representation learning constitutes a significant portion of these methods. By relying on the factorization of an image into independent variation latent codes, this approach offers greater control over the result of the synthesis

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Zhuang, Zhenfeng, Min Cen, Yanfeng Li, et al. "Dynamic Entity-Masked Graph Diffusion Model for Histopathology Image Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 11058–66. https://doi.org/10.1609/aaai.v39i10.33202.

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Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models from natural images to histopathology tasks. Moreover, the frequent lack of annotations in histopathology patch images has driven researchers to explore self-supervised learning methods like mask reconstruction for learning representations from large amounts of unlabeled data. Crucially, previous mask-based efforts in self-supervised learning have often overlooked the spatial interactions among entities, which are es

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Christensen, Andrew J., Ananya Sen Gupta, and Ivars Kirsteins. "Graph representation learning on braid manifolds." Journal of the Acoustical Society of America 152, no. 4 (2022): A39. http://dx.doi.org/10.1121/10.0015466.

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The accuracy of autonomous sonar target recognition systems is usually hindered by morphing target features, unknown target geometry, and uncertainty caused by waveguide distortions to signal. Common “black-box” neural networks are not effective in addressing these challenges since they do not produce physically interpretable features. This work seeks to use recent advancements in machine learning to extract braid features that can be interpreted by a domain expert. We utilize Graph Neural Networks (GNNs) to discover braid manifolds in sonar ping spectra data. This approach represents the sona

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Li, Jingyu, Zhiyong Feng, Dongxiao He, Hongqi Chen, Qinghang Gao, and Guoli Wu. "Contrastive Representation for Interactive Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 12058–66. https://doi.org/10.1609/aaai.v39i11.33313.

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Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented through Deep Reinforcement Learning (DRL), because DRL is inherently compatible with the dynamic nature of IR. However, DRL is currently not perfect for IR. Due to the large action space and sample inefficiency problem, training DRL recommender agents is challenging. The key point is that useful features cannot be extracted as high-quality representations for the recommender agent t

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Cadieu, Charles F., and Bruno A. Olshausen. "Learning Intermediate-Level Representations of Form and Motion from Natural Movies." Neural Computation 24, no. 4 (2012): 827–66. http://dx.doi.org/10.1162/neco_a_00247.

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We present a model of intermediate-level visual representation that is based on learning invariances from movies of the natural environment. The model is composed of two stages of processing: an early feature representation layer and a second layer in which invariances are explicitly represented. Invariances are learned as the result of factoring apart the temporally stable and dynamic components embedded in the early feature representation. The structure contained in these components is made explicit in the activities of second-layer units that capture invariances in both form and motion. Whe

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Sun, Li, Zhongbao Zhang, Jiawei Zhang, et al. "Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4375–83. http://dx.doi.org/10.1609/aaai.v35i5.16563.

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Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representations in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TG

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Ljubešić, Nikola. "‟Deep lexicography” – Fad or Opportunity?" Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, no. 2 (2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.

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In recent years, we are witnessing staggering improvements in various semantic data processing tasks due to the developments in the area of deep learning, ranging from image and video processing to speech processing, and natural language understanding. In this paper, we discuss the opportunities and challenges that these developments pose for the area of electronic lexicography. We primarily focus on the concept of representation learning of the basic elements of language, namely words, and the applicability of these word representations to lexicography. We first discuss well-known approaches

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Zheng, Tingyi, Yilin Zhang, and Yuhang Wang. "Dynamic guided metric representation learning for multi-view clustering." PeerJ Computer Science 8 (March 8, 2022): e922. http://dx.doi.org/10.7717/peerj-cs.922.

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Multi-view clustering (MVC) is a mainstream task that aims to divide objects into meaningful groups from different perspectives. The quality of data representation is the key issue in MVC. A comprehensive meaningful data representation should be with the discriminant characteristics in a single view and the correlation of multiple views. Considering this, a novel framework called Dynamic Guided Metric Representation Learning for Multi-View Clustering (DGMRL-MVC) is proposed in this paper, which can cluster multi-view data in a learned latent discriminated embedding space. Specifically, in the

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Xie, Mingxuan, Tao Zou, Junchen Ye, Bowen Du, and Runhe Huang. "Dynamic Graph Representation Learning for Passenger Behavior Prediction." Future Internet 16, no. 8 (2024): 295. http://dx.doi.org/10.3390/fi16080295.

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Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and public transportation planning. Existing research primarily relies on statistical methods and sequential models to learn from individual historical interactions, which ignores the correlations between passengers and stations. To address these issues, this paper proposes DyGPP, which leverages dynamic graphs to capture the intricate evolution of passen

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Velasquez, Alvaro, Brett Bissey, Lior Barak, et al. "Multi-Agent Tree Search with Dynamic Reward Shaping." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 652–61. http://dx.doi.org/10.1609/icaps.v32i1.19854.

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Sparse rewards and their representation in multi-agent domains remains a challenge for the development of multi-agent planning systems. While techniques from formal methods can be adopted to represent the underlying planning objectives, their use in facilitating and accelerating learning has witnessed limited attention in multi-agent settings. Reward shaping methods that leverage such formal representations in single-agent settings are typically static in the sense that the artificial rewards remain the same throughout the entire learning process. In contrast, we investigate the use of such fo

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Li, Bin, Yunlong Fan, Miao Gao, Yikemaiti Sataer, and Zhiqiang Gao. "A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction." Electronics 12, no. 11 (2023): 2357. http://dx.doi.org/10.3390/electronics12112357.

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Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs’ powerful ability in learning expressive graph representations. However, most of these works learn graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone, and the errors introduced in the static graph cannot be corrected and might accumulate in later stages, and (2) the graph construction stage and graph representation learning stage are disjoined, which negatively affects the model’s running

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Geng, Shijie, Peng Gao, Moitreya Chatterjee, et al. "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.

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Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and produci

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Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which

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Ren, Xiaobin, Kaiqi Zhao, Patricia J. Riddle, Katerina Taskova, Qingyi Pan, and Lianyan Li. "DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–25. http://dx.doi.org/10.1145/3589333.

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Missing data imputation for location-based sensor data has attracted much attention in recent years. The state-of-the-art imputation methods based on graph neural networks have a priori assumption that the spatial correlations between sensor locations are static. However, real-world data sets often exhibit dynamic spatial correlations. This paper proposes a novel approach to capturing the dynamics of spatial correlations between geographical locations as a composition of the constant, long-term trends and periodic patterns. To this end, we design a new method called Dynamic Adjacency Matrix Re

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Achille, Alessandro, and Stefano Soatto. "A Separation Principle for Control in the Age of Deep Learning." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 287–307. http://dx.doi.org/10.1146/annurev-control-060117-105140.

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We review the problem of defining and inferring a state for a control system based on complex, high-dimensional, highly uncertain measurement streams, such as videos. Such a state, or representation, should contain all and only the information needed for control and discount nuisance variability in the data. It should also have finite complexity, ideally modulated depending on available resources. This representation is what we want to store in memory in lieu of the data, as it separates the control task from the measurement process. For the trivial case with no dynamics, a representation can

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Geng, Yu, Zongbo Han, Changqing Zhang, and Qinghua Hu. "Uncertainty-Aware Multi-View Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7545–53. http://dx.doi.org/10.1609/aaai.v35i9.16924.

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Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, quality of data usually varies for different samples (even for different views), i.e., one view may be informative for one sample but not the case for another. Therefore, it is quite challenging to integrate multi-view noisy data under unsupervised setting. Traditional multi-view methods either simply treat each view with equal importance or tune the weights o

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Malloy, Tyler, Yinuo Du, Fei Fang, and Cleotilde Gonzalez. "Generative Environment-Representation Instance-Based Learning: A Cognitive Model." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 326–33. http://dx.doi.org/10.1609/aaaiss.v2i1.27696.

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Instance-Based Learning Theory (IBLT) suggests that humans learn to engage in dynamic decision making tasks through the accumulation of experiences, represented by the decision task features, the actions performed, and the utility of decision outcomes. This theory has been applied to the design of Instance-Based Learning (IBL) models of human behavior in a variety of contexts. One key feature of all IBL model applications is the method of accumulating instance-based memory and performing recognition-based retrieval. In simple tasks with few features, this knowledge representation and retrieval

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Zhang, Xiaoxian, Jianpei Zhang, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.

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The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social netwo

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Lv, Feiya, Chenglin Wen, and Meiqin Liu. "Dynamic reconstruction based representation learning for multivariable process monitoring." Journal of Process Control 81 (September 2019): 112–25. http://dx.doi.org/10.1016/j.jprocont.2019.06.012.

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Yin, Ying, Li-Xin Ji, Jian-Peng Zhang, and Yu-Long Pei. "DHNE: Network Representation Learning Method for Dynamic Heterogeneous Networks." IEEE Access 7 (2019): 134782–92. http://dx.doi.org/10.1109/access.2019.2942221.

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Najafi, Bahareh, Saeedeh Parsaeefard, and Alberto Leon-Garcia. "Entropy-Aware Time-Varying Graph Neural Networks with Generalized Temporal Hawkes Process: Dynamic Link Prediction in the Presence of Node Addition and Deletion." Machine Learning and Knowledge Extraction 5, no. 4 (2023): 1359–81. http://dx.doi.org/10.3390/make5040069.

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This paper addresses the problem of learning temporal graph representations, which capture the changing nature of complex evolving networks. Existing approaches mainly focus on adding new nodes and edges to capture dynamic graph structures. However, to achieve more accurate representation of graph evolution, we consider both the addition and deletion of nodes and edges as events. These events occur at irregular time scales and are modeled using temporal point processes. Our goal is to learn the conditional intensity function of the temporal point process to investigate the influence of deletio

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Jia, Xuzhong, Chenyu Hu, and Guancong Jia. "Cross-modal Contrastive Learning for Robust Visual Representation in Dynamic Environmental Conditions." Academic Journal of Natural Science 2, no. 2 (2025): 23–34. https://doi.org/10.70393/616a6e73.323833.

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This paper proposes a novel cross-modal contrastive learning framework for robust visual representation under dynamic environmental conditions. We address the challenge of maintaining consistent performance across varying environments by introducing a dual-stream architecture that leverages complementary information from visual and contextual modalities. Our framework incorporates three key components: (1) a cross-modal contrastive learning mechanism that establishes correspondences between modalities while preserving their semantic structure, (2) a feature alignment module with cross-modal at

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Lai, Songxuan, Lianwen Jin, Luojun Lin, Yecheng Zhu, and Huiyun Mao. "SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 735–42. http://dx.doi.org/10.1609/aaai.v34i01.5416.

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An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and

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Threja Malhotra, Ashu, and Jasneet Kaur. "Exploring the Role of Technological Representations to Facilitate Mathematics Learning In E-Class." International Journal of Multidisciplinary Research Configuration 1, no. 3 (2021): 01–05. http://dx.doi.org/10.52984/ijomrc1301.

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This paper explores the role that is played by technological representations used in e-classes during pandemic, in promoting interactions among peers and teacher & students in order to provoke mathematical understandings. The analysis is based upon the theoretical framework as the Johonson Mathematical Representation Model which is an extension of Lesh’s Multimodal model of translations amongst the representations. Findings of the study suggest that Constructive tasks which used dynamic pictorial representations were successful in capturing the interest and curiosity among students and pro

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Fang, Yang, Xiang Zhao, Peixin Huang, Weidong Xiao, and Maarten de Rijke. "Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs." ACM Transactions on Information Systems 40, no. 4 (2022): 1–27. http://dx.doi.org/10.1145/3485189.

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Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model, M-DHIN , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embe

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Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.

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Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learni

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Jiang, Linxing Preston, and Rajesh P. N. Rao. "Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex." PLOS Computational Biology 20, no. 2 (2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.

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We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation

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Huang, Ru, Zijian Chen, Jianhua He, and Xiaoli Chu. "Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning." Sensors 22, no. 4 (2022): 1402. http://dx.doi.org/10.3390/s22041402.

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Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a heavy responsibility to online platforms. Current content analysis methods primarily concentrate on non-textual contents, such as images and videos themselves, while ignoring the interrelationship between each user post’s contents. In this paper, we propose a novel framework named community-aware dynamic heterogeneous graph embedding (CDHNE) for relationship assessment, capable of mining heterogeneous information, latent community structure and dyna

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Feng, Pengbin, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi, and Di Lu. "Android Malware Detection via Graph Representation Learning." Mobile Information Systems 2021 (June 4, 2021): 1–14. http://dx.doi.org/10.1155/2021/5538841.

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With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors. There is an urgent need for developing an automatic malware detection approach to prevent the spread of malware. The low code coverage and poor efficiency of the dynamic analysis limit the large-scale deployment of malware detection methods based on dynamic features. Therefore, researchers have proposed a plethora of detection approaches based on abundant static features to provide efficient malware detection. This paper explores the direction of Androi

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Fu, Sichao, Weifeng Liu, Weili Guan, Yicong Zhou, Dapeng Tao, and Changsheng Xu. "Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (2021): 1–13. http://dx.doi.org/10.1145/3412846.

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Over the past few years, graph representation learning (GRL) has received widespread attention on the feature representations of the non-Euclidean data. As a typical model of GRL, graph convolutional networks (GCN) fuse the graph Laplacian-based static sample structural information. GCN thus generalizes convolutional neural networks to acquire the sample representations with the variously high-order structures. However, most of existing GCN-based variants depend on the static data structural relationships. It will result in the extracted data features lacking of representativeness during the c

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Xiang, Xintao, Tiancheng Huang, and Donglin Wang. "Learning to Evolve on Dynamic Graphs (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13091–92. http://dx.doi.org/10.1609/aaai.v36i11.21682.

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Representation learning in dynamic graphs is a challenging problem because the topology of graph and node features vary at different time. This requires the model to be able to effectively capture both graph topology information and temporal information. Most existing works are built on recurrent neural networks (RNNs), which are used to exact temporal information of dynamic graphs, and thus they inherit the same drawbacks of RNNs. In this paper, we propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that jointly learns graph information and time information. Specifically,

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olde Scheper, Tjeerd V. "Criticality Analysis: Bio-Inspired Nonlinear Data Representation." Entropy 25, no. 12 (2023): 1660. http://dx.doi.org/10.3390/e25121660.

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The representation of arbitrary data in a biological system is one of the most elusive elements of biological information processing. The often logarithmic nature of information in amplitude and frequency presented to biosystems prevents simple encapsulation of the information contained in the input. Criticality Analysis (CA) is a bio-inspired method of information representation within a controlled Self-Organised Critical system that allows scale-free representation. This is based on the concept of a reservoir of dynamic behaviour in which self-similar data will create dynamic nonlinear repre

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Wang, Xixi, Bo Jiang, Xiao Wang, and Bin Luo. "Learning Dynamic Batch-Graph Representation for Deep Representation Learning." International Journal of Computer Vision, July 19, 2024. http://dx.doi.org/10.1007/s11263-024-02175-8.

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Duan, Pingtao, Xiangsheng Ren, and Yuting Liu. "Multi-relational dynamic graph representation learning." Neurocomputing, August 2023, 126688. http://dx.doi.org/10.1016/j.neucom.2023.126688.

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