Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Diffusion models: A comprehensive survey of methods and applications
Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …
record-breaking performance in many applications, including image synthesis, video …
Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
Temporal graph benchmark for machine learning on temporal graphs
Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …
Are graph augmentations necessary? simple graph contrastive learning for recommendation
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …
recommendation, since its ability to extract self-supervised signals from the raw data is well …
Improving graph collaborative filtering with neighborhood-enriched contrastive learning
Recently, graph collaborative filtering methods have been proposed as an effective
recommendation approach, which can capture users' preference over items by modeling the …
recommendation approach, which can capture users' preference over items by modeling the …
Uncertainty quantification over graph with conformalized graph neural networks
Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …
Heterogeneous graph contrastive learning for recommendation
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …
data in recommender systems. However, real-life recommendation scenarios usually involve …
A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Prodigy: Enabling in-context learning over graphs
In-context learning is the ability of a pretrained model to adapt to novel and diverse
downstream tasks by conditioning on prompt examples, without optimizing any parameters …
downstream tasks by conditioning on prompt examples, without optimizing any parameters …