Tag: causal machine learning
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Limitations of Protein Sequence Data in Parkinson’s Classification
Explore the challenges of using protein sequence data alone for Parkinson's disease classification and the need for advanced biological features.
Polynomial Expansion Rank Adaptation for Efficient Fine-Tuning
Discover Polynomial Expansion Rank Adaptation (PERA) to enhance low-rank fine-tuning with high-order interactions for better large language model performan...
Layer-wise Insights for Efficient Supervised Fine-Tuning
Discover how selective layer tuning improves supervised fine-tuning, reducing forgetting and boosting model performance by 10%+ on GSM8K.
GRACE: Efficient Dynamic Coreset Selection for LLMs
Discover GRACE, a dynamic coreset selection framework that optimizes large language model training by improving efficiency and maintaining performance.
Cycle-Consistent Search: Training Search Agents Without Gold Data
Discover Cycle-Consistent Search, a scalable method to train search agents using question reconstructability without relying on gold supervision.