Modeling customer shopping intentions is a crucial task for e-commerce, ...
Learning on Graphs has attracted immense attention due to its wide real-...
With the prosperity of e-commerce and web applications, Recommender Syst...
Link prediction attempts to predict whether an unseen edge exists based ...
Molecule discovery plays a crucial role in various scientific fields,
ad...
Recent studies on Graph Neural Networks(GNNs) provide both empirical and...
Recently, Generative Diffusion Models (GDMs) have showcased their remark...
Recent research has highlighted the vulnerability of Deep Neural Network...
The recent development of multimodal single-cell technology has made the...
A fundamental task for knowledge graphs (KGs) is knowledge graph complet...
Diffusion models, as a novel generative paradigm, have achieved remarkab...
The page presentation biases in the information retrieval system, especi...
With more people publishing their personal data online, unauthorized dat...
Fair classification aims to stress the classification models to achieve ...
The existence of adversarial examples brings huge concern for people to ...
Graph Neural Networks (GNNs) have made tremendous progress in the graph
...
As powerful tools for representation learning on graphs, graph neural
ne...
Knowledge graphs (KGs) have gained prominence for their ability to learn...
The unbiased learning to rank (ULTR) problem has been greatly advanced b...
Knowledge tracing (KT) is the task of using students' historical learnin...
As training deep learning models on large dataset takes a lot of time an...
Recent years have witnessed remarkable success achieved by graph neural
...
Federated learning is considered as an effective privacy-preserving lear...
Knowledge graphs (KGs) facilitate a wide variety of applications due to ...
Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a
co...
Graph Neural Networks (GNNs) have made rapid developments in the recent
...
Query understanding plays a key role in exploring users' search intents ...
Recent advances in multimodal single-cell technologies have enabled
simu...
Graph neural networks (GNNs) have been widely applied in the recommendat...
Since the fatal shooting of 17-year old Black teenager Trayvon Martin in...
Recommender systems aim to provide personalized services to users and ar...
Decentralized optimization and communication compression have exhibited ...
Graph Neural Networks (GNNs) have boosted the performance for many
graph...
Adversarial training has been empirically proven to be one of the most
e...
Sentence completion (SC) questions present a sentence with one or more b...
In this work, we study computational approaches to detect online dialogi...
In the past few decades, artificial intelligence (AI) technology has
exp...
While many existing graph neural networks (GNNs) have been proven to per...
Designing an effective loss function plays a crucial role in training de...
Graph neural networks (GNNs) have shown great prowess in learning
repres...
Graph self-supervised learning has gained increasing attention due to it...
Recent studies suggest that “memorization” is one important factor for
o...
Graph neural networks (GNNs) have received tremendous attention due to t...
It is evident that deep text classification models trained on human data...
Graph Neural Networks (GNNs) have achieved tremendous success in various...
The automatic evaluation for school assignments is an important applicat...
Adversarial training algorithms have been proven to be reliable to impro...
Graph Neural Networks (GNNs) have risen to prominence in learning
repres...
Dialogue systems play an increasingly important role in various aspects ...
Representation learning has been proven to play an important role in the...