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- Graph WaveNet for Deep Spatial-Temporal Graph Modeling3,648
This paper proposes a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling by developing a novel adaptive dependency matrix and learn it through node embedding, which can precisely capture the hidden spatial dependency in the data.
- Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks2,376
This paper proposes a general graph neural network framework designed specifically for multivariate time series data that outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
- FedProto: Federated Prototype Learning across Heterogeneous Clients1,069
A novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients, with FedProto outperforming several recent FL approaches on multiple datasets.
- Adversarially Regularized Graph Autoencoder for Graph Embedding871
A novel adversarial graph embedding framework for graph data that encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure.
- DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding794
A novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise) and a light-weight neural net is proposed, based solely on the proposed attention without any RNN/CNN structure, which outperforms complicated RNN models on both prediction quality and time efficiency.
- Attributed Graph Clustering: A Deep Attentional Embedding Approach669
This paper proposes a goal-directed deep learning approach to graph clustering, Deep Attentional Embedded Graph Clustering (DAEGC), which focuses on attributed graphs to sufficiently explore the two sides of information in graphs.
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- MGAE: Marginalized Graph Autoencoder for Graph Clustering537
A marginalized graph convolutional network is proposed to corrupt network node content, allowing node content to interact with network features, and marginalizes the corrupted features in a graph autoencoder context to learn graph feature representations.
- Adversarially Regularized Graph Autoencoder.504
A novel adversarial graph embedding framework for graph data that encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure.
- Multi-center federated learning: clients clustering for better personalization399
A novel multi-center aggregation mechanism to cluster clients using their models’ parameters is proposed that learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers and outperforms several popular baseline methods.
- Federated Learning for Open Banking385
This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.
- Learning Graph Embedding With Adversarial Training Methods363
This article presents a novel adversarially regularized framework for graph embedding, employing the graph convolutional network as an encoder, that embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph.
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- Supervised Learning for Suicidal Ideation Detection in Online User Content212
An approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning byalysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies.
- Learning Private Neural Language Modeling with Attentive Aggregation180
This work proposes a novel model aggregation with an attention mechanism considering the contribution of client models to the global model, together with an optimization technique during server aggregation that minimizes the weighted distance between the server model and client models.
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- Emerging trends in federated learning: from model fusion to federated X learning159
Various learning algorithms to improve the vanilla federated averaging algorithm are explored and model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods are reviewed.
- Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling148
An RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA is proposed, which achieves state-of-the-art performance on both the Stanford Natural Language Inference and the Sentences Involving Compositional Knowledge datasets.
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- Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline.130
A novel Omni-Scale 1D-CNN (OS-CNN) architecture is proposed which enables the model to capture the proper kernel size during the model learning period and a specific design for kernel size configuration is developed which enables it to assemble very few kernel-size options to represent more receptive fields.
- A Survey on Deep Learning based Time Series Analysis with Frequency Transformation113
A comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT, exploring the primary approaches used in current models that incorporate FT, the types of neural networks that leverage FT, and the representative FT-equipped models in deep time series analysis.
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- Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction90
A brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework is proposed, which achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics.
- Attribute Propagation Network for Graph Zero-Shot Learning88
The “attribute propagation network (APNet)” is introduced, which is composed of a graph propagation model generating attribute vector for each class and a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding.
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- Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base85
This work proposes an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model.
- Personalized Federated Learning With a Graph84
This paper proposes a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data and casts SFL with graph into a novel optimization problem that can model the Client-wise complex relations and graph-based structural topology by a unified framework.
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- Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health78
This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI.
- Re-Reading Improves Reasoning in Large Language Models74
RE2 demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT, and its adaptability is revealed, showing how it can be effectively integrated with different LLMs, thought-eliciting prompting, and ensemble strategies.
- Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning74
This work proposes an entity masking scheme that exploits relational knowledge underlying the text, and uses knowledge graphs to obtain distractors for the masked entities, and proposes a novel distractor-suppressed ranking objective which is optimized jointly with masked language model.
- Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph73
It is shown that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification, and PPN significantly outperforms most recent few- shot learning methods in different settings, even when they are also allowed to train on weak- labeled data.
- Safe Medicine Recommendation via Medical Knowledge Graph Embedding73
Safe Medicine Recommendation (SMR) is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world and jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space.
- GPFedRec: Graph-Guided Personalization for Federated Recommendation72
A novel Graph-guided Personalization for Federated Recommendation (GPFedRec) that can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe is validated.
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- Deep neighbor-aware embedding for node clustering in attributed graphs67
A clustering-directed deep learning approach, Deep Neighbor-aware Embedded Node Clustering ( DNENC for short) for clustering graph data, which encodes the topological structure and node content in a graph into a compact representation via a neighbor-aware graph autoencoder, which progressively absorbs information from neighbors via a convolutional or attentional encoder.
- DAGCN: Dual Attention Graph Convolutional Networks66
DAGCN automatically learns the importance of neighbors at different hops using a novel attention graph convolution layer, and then employs a second attention component, a self-attention pooling layer, to generalize the graph representation from the various aspects of a matrix graph embedding.
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- Robust Semi-Supervised Learning through Label Aggregation58
This paper proposes an efficient RObust Semi-Supervised Ensemble Learning (ROSSEL) method, which generates pseudo-labels for unlabeled data using a set of weak annotators, and combines them to approximate the ground-truth labels to assist semi-supervised learning.
- NeuRec: On Nonlinear Transformation for Personalized Ranking57
A neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors is proposed.
- Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering55
This paper proposes Automatic Graph Convolutional Networks (AutoGCN) to capture the full spectrum of graph signals and automatically update the bandwidth of graph convolutional filters, based on graph spectral theory.
- Distributionally Robust Semi-Supervised Learning for People-Centric Sensing53
This work proposes a generic distributionally robust model for semi-supervised learning on distributionally shifted data that learns the latent features that reduce person-specific discrepancy and preserve task-specific consistency.
- Interpretable Time-series Classification on Few-shot Samples52
An interpretable neural-based framework, namely Dual Prototypical Shapelet Networks (DPSN) for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity.
- Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining52
This study suggests that problems in the automated diagnosis code annotation can be reliably addressed by using a multi-label learning model that exploits disease correlation.
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- Dynamic Concept Composition for Zero-Example Event Detection51
This paper focuses on automatically detecting events in unconstrained videos without the use of any visual training exemplars, and proposes to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with free-form text descriptions of their content.
- Causal Reinforcement Learning: A Survey49
This survey comprehensively review the literature on causal reinforcement learning and introduces the basic concepts of causality and reinforcement learning, and explains how causality can address core challenges in non-causal reinforcement learning.
- TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting48
This article proposes TraverseNet, a novel spatial–temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial–temporal graphs while exploiting the evolving spatial–temporal dependencies for each node via message traverse mechanisms.
- Task Aligned Generative Meta-learning for Zero-shot Learning47
This work proposes a novel Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ), aiming to mitigate the potentially biased training and to enable meta-ZSL to accommodate real-world datasets that contain diverse distributions.
- Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning44
This work proposes a contrastive learning-based FL framework, namely FedICON, to capture invariant knowledge among heterogeneous clients and consistently tune the model to adapt to test data and extensive experiments validate the effectiveness of the proposed FedICON in taming heterogeneity to handle test-time.
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- Decentralized Knowledge Acquisition for Mobile Internet Applications41
A new attention-augmented federated learning framework that can conduct decentralized knowledge acquisition for mobile Internet application scenarios, such as mobile keyboard suggestions, and performs better than other baseline methods in terms of perplexity and communication cost.
- Multi-modality Sensor Data Classification with Selective Attention41
This paper turns this classification task into a game and applies a deep reinforcement learning scheme to dynami-cally deal with complex situations, and intro-duce a selective attention mechanism into the rein-forcement learning Scheme to focus on the crucial dimensions of the data.
- Finding the best not the most: regularized loss minimization subgraph selection for graph classification41
A regularized loss minimization-driven feature selection method for graph classification which integrates subgraph selection and model learning into a unified framework to find discriminative subgraphs with guaranteed minimum loss w.r.t. the objective function is proposed.
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- Sequential Diagnosis Prediction with Transformer and Ontological Representation36
An end-to-end robust transformer-based model called SETOR is proposed, which exploits neural ordinary differential equation to handle both irregular intervals between a patient’s visits with admitted timestamps and length of stay in each visit, and to alleviate the limitation of insufficient data by integrating medical ontology.
- Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy36
The proposed model, “memory-augmented hierarchical-classification network (MahiNet)”, performs coarse-to-fine classification where each coarse class can cover multiple fine classes and outperforms several state-of-the-art models on MCFS problems in both supervised learning and meta-learning.
- Temporal Self-Attention Network for Medical Concept Embedding35
This work proposes a novel attention mechanism which captures the contextual information and temporal relationships between medical concepts and is the first to exploit temporal self-attentive relations between medical events.
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- Multiple graph unsupervised feature selection32
The results support that the multiple graph could be better than the single graph in the unsupervised feature selection, and the overall performance of the proposed method is higher than the other comparisons.
- Clustering social audiences in business information networks30
A novel machine-learning approach that co-clusters business information networks to discover and understand these audiences, called CBIN, which is based on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data.
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- Going Deep: Graph Convolutional Ladder-Shape Networks29
The proposed graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance, is presented.
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- Does Continual Learning Equally Forget All Parameters?27
This paper studies which modules in neural networks are more prone to forgetting by investigating their training dynamics during continual learning (CL), and finds that finetuning them only on a small buffer at the end of any CL method can bring non-trivial improvement.
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- FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels25
A simple two-level sampling method that selects clean labels and correct pseudo-labels at the client end for more robust local training and investigates different schedules changing the local epochs between aggregations over the course of FL, which notably improves the communication and computation efficiency in noisy label setting.
- Federated Low-Rank Adaptation for Foundation Models: A Survey24
This survey examines how LoRA has been integrated into federated fine-tuning for foundation models—an area known as FedLoRA by focusing on three key challenges: distributed learning, heterogeneity, and efficiency.
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- Detecting Suicidal Ideation with Data Protection in Online Communities24
This work proposes a novel data-protecting solution to learn a model to train a local data-preserving model for each user which only shares their own model’s parameters with the server rather than their personal information.
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- Continual Task Allocation in Meta-Policy Network via Sparse Prompting18
Continual Task Allocation via Sparse Prompting (CoTASP), which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network, achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences.
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- Boosting for graph classification with universum16
A new universum graph classification framework which leverages additional “non-example” graphs to help improve the graph classification accuracy and proposes a mathematical programming algorithm, ugBoost, which integrates discriminative subgraph selection and margin maximization into a unified framework to fully exploit the universum.
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- Universal network representation for heterogeneous information networks7
A universal network representation approach (UNRA), that represents different types of nodes in heterogeneous information networks in a continuous and common vector space, and achieves outstanding performance, compared to six other state-of-the-art algorithms.
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- Graph Ladder Networks for Network Classification6
This paper proposes an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks which unifies the representation learning and network classification into one single framework by exploiting both labeled and unlabeled nodes in a network.
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- Personalized Federated Learning With Graph3
A novel structured federated learning framework to simultaneously learn the global model and personalized model using each client’s local relations with others and its private dataset and the effectiveness of the proposed method has been demonstrated in varying degrees of data non-iid settings.
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- Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care.2
A decentralized learning framework to enable proactive social care equipped with artificial intelligence and data protection, and a novel model aggregation algorithm, namely the average difference descent aggregation (AvgDiffAgg for short).
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- FL@FM-TheWebConf'25: International Workshop on Federated Foundation Models for the Web 2025–
The FL@FM-TheWebConf'25 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field of machine learning research.
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- FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web–
The FL@FM-TheWebConf'24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field of machine learning.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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