Papers with predictive modeling
Automated Screening of Antibacterial Nanoparticle Literature: Dataset Curation and Model Evaluation (2026.eacl-long)
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Alperen Ozturk, Şaziye Betül Özateş, Sophia Bahar Root, Angela Violi, Nicholas Kotov, J. Scott VanEpps, Emine Sumeyra Turali Emre
| Challenge: | Antimicrobial resistance is a growing global health threat, driving interest in nanoparticle-based alternatives to conventional antibiotics. |
| Approach: | They propose to use machine learning to classify scientific abstracts using inorganic nanoparticles with intrinsic antibacterial properties. |
| Outcome: | The proposed method distinguishes intrinsic antibacterial NPs from studies focusing on drug carriers or surface-bound applications. |
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization (2023.emnlp-industry)
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Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, Jianwei Zhang
| Challenge: | Tabular data analysis is an important application task of large language models, but advanced models are not yet on par with expert level performance. |
| Approach: | They propose to employ Large Language Models to facilitate an automated guide and execute high-precision data analyzes on tabular datasets. |
| Outcome: | The proposed framework is based on large language models and an automated machine learning pipeline for predictive modeling. |
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora (2020.findings-emnlp)
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| Challenge: | Existing research on knowledge transfer focuses on documents as unit of analysis and follow their transfer into practice for a specific scientific domain. |
| Approach: | They analyze scientific concepts from corpora and use them to predict knowledge transfer . they find that only a small proportion of these ideas will be used in inventions . |
| Outcome: | The proposed model predicts the use of scientific concepts in clinical trials and inventions. |
How to Set the Learning Rate for Large-Scale Pre-training? (2026.findings-acl)
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| Challenge: | Optimal configuration of the learning rate (LR) is a fundamental yet formidable challenge in large-scale pre-training. |
| Approach: | They propose a Fitting Paradigm and a Transfer Paradigme to investigate fit and transfer . they propose scalability and elucidate the reasons why module-wise parameter tuning underperforms . |
| Outcome: | The proposed model reduces the search complexity by reducing the search cost by lowering the search factor. |