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Found 5 papers, 1 papers with code
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science
This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science.
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
To further strengthen the applicability of diffusion models to limited training data, we investigate the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogen atoms to target different data distributions.
Equivariant Graph Attention Networks for Molecular Property Prediction
Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery.
Unsupervised Learning of Group Invariant and Equivariant Representations
In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning.
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs.
Ranked #15 on
Graph Property Prediction
on ogbg-molpcba
