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Found 5 papers, 3 papers with code
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
In this paper, we focus on addressing the issues of topic granularity and hallucinations for better LLM-based topic modelling.
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction
Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular representations from unlabeled data.
Interpretable bilinear attention network with domain adaptation improves drug-target prediction
Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution.
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems.
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.
