Multitask Extension of Geometrically Aligned Transfer Encoder
This work addresses data scarcity in molecular science, but it appears incremental as it extends an existing method to a multi-task context.
The paper tackles the problem of limited data in molecular datasets by extending the Geometrically Aligned Transfer Encoder (GATE) to a multi-task setup, leveraging mutual information across tasks to improve performance on target data through geometric alignment of encoding spaces.
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.