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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.
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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