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Found 7 papers, 1 papers with code
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery
Drug discovery is crucial for identifying candidate drugs for various diseases. However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem.
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery
To overcome this challenge, we present a novel method named contextual representation anchor Network (CRA), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness.
Dual-Label Learning With Irregularly Present Labels
DLL features a dual-tower model architecture that explicitly captures the information exchange between labels, aimed at maximizing the utility of partially available labels in understanding label correlation.
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs
To our knowledge, this is the first work to integrate KANs into GNN architectures tailored for molecular representation learning.
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
The Semi-supervised Teeth Segmentation (STS) Challenge, a pioneering event in tooth segmentation, was held as a part of the MICCAI 2023 ToothFairy Workshop on the Alibaba Tianchi platform.
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data
The advantage also holds for scattered missing data at high missing rates.
