Multi-Task Latent Space Alignment for Small Molecular Data
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Solution Overview
Problem
Existing transfer learning techniques struggle with small, complex data sets such as molecular structure data, particularly in non-Euclidean spaces, leading to inadequate performance and stability in high-dimensional problems.
Innovation Solution
A multi-tasking model training method that utilizes geometric alignment in an integrated latent space to transfer and learn knowledge across tasks, incorporating geometric alignment vectors, perturbation vectors, and multiple loss functions to optimize model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing Euclidean space-based transfer learning techniques are used, then the model can be applied to new domains, but the prediction performance and stability are inadequate on small, complex data sets like molecular structure data
Solution Approach 1:
The patent changes the fundamental parameter of the feature space from Euclidean to Riemannian manifold, allowing the model to better capture complex relationships in molecular structure data while maintaining transfer learning capabilities across domains
Solution Approach 2:
The patent introduces curved manifold geometry to represent latent spaces, where the curvature captures the intrinsic structure of molecular data. This geometric transformation enables better generalization to new domains by preserving structural relationships that flat Euclidean space cannot represent
2Quantity of substance
If transfer learning is applied to small data sets, then the need for large amounts of data is reduced, but the handling of complex structures in non-Euclidean spaces becomes inadequate
Solution Approach 1:
The patent transforms the data representation parameter from Euclidean vectors to Riemannian manifold points, enabling effective transfer learning on small data sets by capturing complex molecular structures through geometric properties rather than requiring large volumes of training data
3Adaptability or versatility
If geometric alignment in integrated latent space is implemented, then knowledge transfer between tasks is improved, but the model complexity increases
Solution Approach 1:
The patent creates a universal Riemannian manifold framework that serves multiple tasks simultaneously. The geometric alignment mechanism provides a unified approach for knowledge transfer across different molecular properties and domains, reducing the need for task-specific model components despite the increased geometric complexity
Data Source
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AI summary
A multi-tasking model training method and a multi-tasking performing method using a machine learning model trained on the basis thereof, according to an embodiment of the present invention, may mutually transfer and learn knowledge data of a latent space for each task through geometric alignment in one integrated latent space in order to process a multi-task for output according to a plurality of domains.