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Found 11 papers, 11 papers with code
Stoichiometry Representation Learning with Polymorphic Crystal Structures
Despite the recent success of machine learning (ML) in materials science, its success heavily relies on the structural description of crystal, which is itself computationally demanding and occasionally unattainable.
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy.
Task Relation-aware Continual User Representation Learning
However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks.
Task-Equivariant Graph Few-shot Learning
To solve this problem, it is important for GNNs to be able to classify nodes with a limited number of labeled nodes, known as few-shot node classification.
Shift-Robust Molecular Relational Learning with Causal Substructure
To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables.
Conditional Graph Information Bottleneck for Molecular Relational Learning
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications.
Predicting Density of States via Multi-modal Transformer
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials.
Heterogeneous Graph Learning for Multi-modal Medical Data Analysis
In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data.
Relational Self-Supervised Learning on Graphs
Despite their success, existing GRL methods tend to overlook an inherent distinction between images and graphs, i. e., images are assumed to be independently and identically distributed, whereas graphs exhibit relational information among data instances, i. e., nodes.
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment
Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i. e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes.
Augmentation-Free Self-Supervised Learning on Graphs
Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods.
