Multitask Extension of Geometrically Aligned Transfer Encoder

2024/05/03 by Sung Moon Ko, Sumin Lee, Ko, Sung Moon +11 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #Iterative Learning Control Systems #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM) #Real-time simulation and control systems #Sensor Technology and Measurement Systems

paper · pdf · doi:10.48550/arxiv.2405.01974

openalex publication_date 2024/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Molecular datasets often suffer from a lack of data. It is well-known that gathering data is difficult due to the complexity of experimentation or simulation involved. Here, we leverage mutual information across different tasks in molecular data to address this issue. We extend an algorithm that utilizes the geometric characteristics of the encoding space, known as the Geometrically Aligned Transfer Encoder (GATE), to a multi-task setup. Thus, we connect multiple molecular tasks by aligning the curved coordinates onto locally flat coordinates, ensuring the flow of information from source tasks to support performance on target data.

Cited by

• Towards a Generalizable AI for Materials Discovery: Validation through Immersion Coolant Screening
• Geometric Embedding Alignment via Curvature Matching in Transfer Learning

Related

• Task Addition in Multi-Task Learning by Geometrical Alignment
• Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
• Geometric Embedding Alignment via Curvature Matching in Transfer Learning
• nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder
• libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications