10 accepted papers
Multimodal sequential recommender systems leverage diverse modal inputs to enhance the accuracy and relevance of personalized recommendations. However, existing fusion strategies often struggle to capture intricate cross-modal interactions, especially under the evolving dynamics of user intent. More
Recently, contrastive learning has shown promising results for representing graphs. Despite their success, several key issues have not been well addressed in existing studies: 1) Data noise and incompleteness are inevitable in the graph signal due to various factors. 2) Improper augmentation strateg…
Aspect is a valuable tool for determining the perspective from which an event is observed, allowing for viewing both at the situation and viewpoint level. Uniform Meaning Representation (UMR) seeks to provide a standard, typologically-informed representation of aspects across languages. It employs a…
The educational disparities among different regions are remarkably significant. The educational resource platform can effectively bridge the educational capability gap between regions. Most of the existing recommendation algorithms only consider interaction history, while we argue that the dependenc…
The explainability of deep neural networks (DNNs) is critical for trust and reliability in AI systems. Path-based attribution methods, such as integrated gradients (IG), aim to explain predictions by accumulating gradients along a path from a baseline to the target image. However, noise accumulated…
Robust and accurate traffic forecasting is a key issue in intelligent transportation systems. Existing studies usually employ pre-defined spatial graph or learned fixed adjacency graph and design models to capture spatial and temporal features. However, pre-defined or fixed graph can not accurately…
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms still have the following limitation. First, the scalability limitation precludes the br…
Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is salience-based visual interpretation, such as Grad-CAM, where the generation of attention maps depends merely on categoric…
Sparse-view computed tomography (CT)---using a small number of projections for tomographic reconstruction---enables much lower radiation dose to patients and accelerated data acquisition. The reconstructed images, however, suffer from strong artifacts, greatly limiting their diagnostic value. Curren…
Gait is considered the walking pattern of human body, which includes both shape and motion cues. However, the main-stream appearance-based methods for gait recognition rely on the shape of silhouette. It is unclear whether motion can be explicitly represented in the gait sequence modeling. In this p…