Learning with Limited Data

Good machine learning is heavily dependent on good data. A few more good data-points is likely to be worth billions of model parameters. 

 However, sometimes we need to train models when data is limited. There are a number of strategies that we can try. 

 Zero-Shot and Few-Shot Learning 

   

 

 Pattern Exploitative Training is a way to use a small number of examples to train text classifiers. It is technically an example of synthetic data generation.  

 

 

 In Context Learning (ICL) 

 

 Synthetic Data Generation and Augmentation