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Large-scale general domain pretraining followed by downstream-specific finetuning has become a predominant paradigm in machine learning. However, discrepancies between the pretraining and target domains can still lead to performance degradation in certain cases, underscoring the need for task-adaptive continued pretraining (TAP). TAP methods typically involve continued pretraining on task-specific unlabeled datasets or introducing additional unsupervised learning objectives to enhance model capabilities. While many TAP methods perform continued pretraining with multiple pretraining objectives, they often determine the tradeoff parameters between objectives manually, resulting in suboptimal outcomes and higher computational costs. In this paper, we propose TapWeight, a task-adaptive pretraining framework which automatically determines the optimal importance of each pretraining objective based on downstream feedback. TapWeight reweights each pretraining objective by solving a multi-level optimization problem. We applied TapWeight to both molecular property prediction and natural language processing tasks, significantly surpassing baseline methods. Experimental results validate the effectiveness and generalizability of TapWeight.
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This content will become publicly available on May 14, 2026
Improving reaction prediction through chemically aware transfer learning
Pretraining of NERF models on chemically related mechanisms significantly improves the performance compared to pretraining by larger, mechanistically dissimilar reaction datasets.
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- Award ID(s):
- 2202693
- PAR ID:
- 10598426
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Digital Discovery
- Volume:
- 4
- Issue:
- 5
- ISSN:
- 2635-098X
- Page Range / eLocation ID:
- 1232 to 1238
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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