Search Results for author:
Found 7 papers, 3 papers with code
Orca 2: Teaching Small Language Models How to Reason
Research on training small LMs has often relied on imitation learning to replicate the output of more capable models.
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MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents
Learning semantically meaningful representations from scientific documents can facilitate academic literature search and improve performance of recommendation systems.
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization
In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning.
Progressive Prompts: Continual Learning for Language Models
We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models.
Learning multi-scale functional representations of proteins from single-cell microscopy data
Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins.
Representation Projection Invariance Mitigates Representation Collapse
In this paper, we propose Representation Projection Invariance (REPINA), a novel regularization method to maintain the information content of representation and reduce representation collapse during fine-tuning by discouraging undesirable changes in the representations.
Multi-defect microscopy image restoration under limited data conditions
Deep learning methods are becoming widely used for restoration of defects associated with fluorescence microscopy imaging.
